Fourth International AAAI Conference on Weblogs and Social Media (ICWSM-10)

Michael Kearns Keynote

Experiments: Graph Coloring / Consensus / Voting

Topology of the Network vs. what was the network used for?

Voting experiments – similar to consensus, with a crucial strategic difference.

Introduce a tension between:

-Individual preferences

-Collective unity

-Color choices; challenge comes from competing incentives

Red, blue. People unaware of global network structure

Payoffs: if everyone picks same color w/in 2 minutes, experiment ends, and everyone gets some payoff. But different players have different incentives (e.g. I may get paid p if everyone converges to blue, but 2p if everyone converges to red). If there is no consensus, nobody gets a payoff

Systems point: confuse player perceptions of system so that players don’t lock into a consensus early on

Results: Varied homophily, random vs. PA ties. 27 total experiments

Result 1: ~70% experiments solved

Result 2: a minority of hubs in a PA networks will dictate the preferences of the network (24/27 experiments converged, 100% of converged picked the minority preference)

In general, it seems to help (in terms of decreasing convergence time) to have one part of the population “care more” about their preference

Effects of “Personality” – people will be stubborn and hold out for their color even when it’s clearly in the minority.

Lessons Learned, 2005-2009

1. People are remarkably good over large set of collective tasks and network topologies (over all experiments, efficiency close to 90%)

2. Network structure matters, often on a task-specific basis

3. Problem – exogenously imposing network on subjects

-are “hard” network structures just unlikely to arise in the real world?

–Network formation games

New experiments: biased voting game + network formation

-everybody starts off as a single vertex and can’t see anyone else’s color

-at any point, players can spend money to purchase edges (money deducted from final winnings in game)

-you are shown all your neighbors, plus all other nodes in a grid. For nodes that are not your neighbors you are shown their degree and current distance away from you

Strategic tensions:

1. Should you buy edges or not? Ideally, want neighbors to buy edges for you, but need a MST to coordinate on task

2. Buy edges for information or for influence?

3. Buy early or late?

4. Buy from high degree or low degree people?

Experimental Designs: 63 experiments, no network to begin with. Additionally, ran 36 experiments where a network structure existed at beginning of experiment but edges could still be bought

Early results: Subjects do quite poorly at network formation games relative to any previous experiments! (47% in first set of tasks, 38% in second set of tasks)

–preliminary evidence shows that people are building networks that make it difficult to solve the biased voting problem

***Sentiment and Language Analysis***

ICWSM – A Great Catch Name: Semi-Supervised Recognition of Sarscastic Sentences in Online Product Reviews (Tsur et al.)

Challenge: Many different definitions beyond the basic one

–Context

–World knowledge

How do people cope?

-Temherte slaq (Some Ethiopic Languages): inverse exclam

-Reverse question mark

-#sarcasm

Data:

-Amazon product reviews (~66K)

–Books, Electronics

-Additional study based on ~6 mln tweets

Star Sentiment Baseline (Amazon)

-”Saying or writing the opposite of what you mean”

–Find unhappy reviewers, look for overwhelmingly positive sentiment

SASI: Semi-supervised Algorithm for Sarcasm Identification

-Label sarcasting-tagged sentences. Tags 1-5 (for different levels of sarcasm)

-Extract features from all training sentences

-represent training sentences in feature space, do KNN

Preprocessing: [author],[title],[product],[company]

Pattern-based features:

–High Frequency Words,

–Content Words

–pattern e.g. {[Frequent] [CW]}*

Weights of pattern based features:

-1: exact match

-alpha – extra elements are found between components

-gamma – incomplete match

Punctuation based features: Number of !, CAPITALIZED words/letters

Classification: weighted-kNN

Experiment 1: 5-fold cross validation on training set: F Score up to .827

Experiment 2: Gold Standard evaluation

–Human annotation of classification of new sentences: F Score up to .788

—F Score improves if you use algorithm on Tweets! (to .827)

Widespread Worry and the Stock Market (Gilbert and Karahalios)

Lab experiments in psych & behavioral econ

–Emotions affect our choices at dcision time

–Fear affects our choices, makes us risk-averse

If we estimate worry and fear, can that tell us anything about the stock market?

–Stock market is probably not efficient (e.g. more likely to go up on a sunny day than down)

–Online media have predictive information

Data: 2008 Livejournal: Feb-Jun, Aug-Sep, Nov-Dec

Why LJ? Place where people talk about their daily lives

Training data: the anxiety index

620K mood-annotated LJ posts. Picked “anxious, worried, nervous, fearful.” = 13K

C1 = Boosted decision tree with top 100 stems

C2 = Complement Naive Bayes

Both classifiers have low true positive rates

Re-mapped to low-frequency data: max of both classifier to label trading day t

Market data: SP_t = S&P-500 closing price

Controlled for volume and volatility of stock market

Method: Granger Causality (Autoregressive Approach, F test)

Result: Adding in anxiety index explains more significantly variance than baseline autoregressive model

claim: estimating worry and fear seems to have some information about market direction

Star Quality: Aggregating Reviews to Rank Products and Merchants (McGlohon, Glance, Reiter)

Google product search

The problem: given reviews, aggregated from different sources, how to measure “true quality” of product. What is the gold standard?

Challenges:

-Different sources have different review scales

-Different sources have different rating distributions

-Reviews may be plagiarized or irrelevant (cf. Danescu-Niculescu-Muzyl 2009)

Outline:

-Analyze ratings aggregated from many review sites

-Propose models to determine “true quality”

-Build evaluation framework

Data:

-Product reviews: 8M ratings (560K products, 3.8M products, 230 sources)

Observation 1: People like passing out 5′s, single-review authors disproportionately more so

Observation 2: Authors / Sources have biases

-Ratings for same product differ widely

-Authors are consistent across products (Like everything or hate everything)

-Sites vary (pricegrabber = 4.5 stars, another site = 2.9 stars average)

Observation 3: The rated object matters

Merchant reviews more “binary”

Netflix more “normal”

Observation 4: How much an object is rate matters (rich-get-richer)

Proposed Models:

1. Mean rating for an object (baseline)

2. Median rating for an object

3. Lower bound on normal confidence interval

4. Binomial confidence interval

5. Average percentile of order statistic (“most websites liked it better than other products”)

6. Filtering anonymous reviews, then average

7. Filter prolific authors, then average

8. Rate authors by reliability

Evaluation Method

-No “ground truth” for quality

-Goal: to see how reliably our ranking of “true quality” agrees with user preferences

-Hold out a pair of ratings from the same author, test on the hold-outs

-For every “prolific” author  hold out two pairs of reviews at random for test data

-Then in training data, calculate estimated quality, rank objects accordingly

-Then compare the given ranking with ranking in each pair in test data

-Results: No method significantly outperforms average rating!

Fourth International AAAI Conference on Weblogs and Social Media (ICWSM-10)

***Microblogging 2***

Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment (Tumasjan et al.)

Successful use of social media in las presidential campaign has established twitter as an integral part of political campaign toolbox

Goal: analyze on Twitter: 1. Deliberation, 2. Sentiment, 3. Prediction

Previous work:

Deliberation: Honeycutt and Herring – Twitter not only used for one-way comm, but 31% of all tweets direct a specific addressee. Kroop and Jansen – political internet discussion boards dominated by small # of heavy users

Sentiment: How accurately can Twitter inform us about the electorate’s political sentiment?

Prediction: can Twitter serve as a predictor of the election result?

Data: examined more than 100k tweets and extracted their sentiment using LIWC

Target: German federal election 2009

Results:

1. While Twitter is used as a forum for political deliberation on substantive issues, this forum is dominated by heavy users

Two widely accepted indicators of blog-based deliberation:

-The exchange of substantive issues (31% of all messages contain “@”),

-Equality of participaion: While the distribution of users across groups is almost identical with the one found on internet message boards, we find even less equality of participation for the political debate on Twitter. Additional analyses have shown users to exhibit a party-bias in the volume and sentiment of messages.

2. The online sentiment in tweets reflects nuanced offline differences between the politicians in our sample.

LIWC profiles:

-Leading candidates: Very similar profile for all leading candidates, only polarizing political characters, such as liberal leader and socialist, deviate in line with their roles as opposition leaders. Messages mentioning Steinmeir (coalition leader) are most tentative

3. Similarity of profiles is a plausible reflection of the political proximity between the parties

Key findings: high convergence of leading candidates, more divergence among politicians of governin grand coalition than among those of a potential right wing coalition

4. Activity on Twitter prior to election seems to validly reflect the election outcome (MAE 1.65%), and joint party mentions accurately reflect the political ties between parties.

From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series (Brendan O’Connor)

Measuring public opinion through social media

Old method – query via dialing, asking, etc.

New method – people write their thoughts to social media, query social media to create aggregate text sentiment measure.

Can compare results from new method to old method

Contributions:

-High correlations between very simple sentiment analysis and telephone polls

-Time series smoothing helps

Text Data: Twitter

-Large, public, ll in one place

-Sources: Archiving Twitter Streaming API (“Gardenhose”/”Sample” ~15% public tweets); Scrape earlier messages via API

-Volume ~ .7B tweets

-Poll data: consumer confidence (2008-2009) – index of consumer sentiment (Reuters/Michigan), Gallup daily. 2008 presidential elections (aggregation, pollster.com). 2009 presidential job approval (Gallup daily)

-Message selection via topic keywords

-topic frequencies change rapidly

-Sentiment analysis: word counting.

–Subjectivity Clues lexicon from OpinionFinder / U Pitt (Very simple system!)

Key: don’t need to classify individual messages correctly, just need a sentiment ratio over messages.

-Sentiment Ratio Moving Average: High day-to-day volatility. Average last k days.

-Which leads, poll or text?

–Cross-correlation analysis: between sentiment score for day t, poll for day t+L.

—Results: “jobs” text leading indicator for poll, can be turned into forecasting model

—Reminiscent of Leskovec et al. Blogpulse paper, very nice!

-Keyword message selection:

–15-day windows, no lag. “jobs” r=80%, “job” r=7%. Is stemming always good?

Presidential elections and job approval: sentiment doesn’t correlate, but pure volume does (79% for “obama” 74% for “mccain”)

Conclusions:

-Preliminary results that sentiment analysis on Twitter data can give information similar to traditional opinion polls. But, still not well-understood. Twitter bias? News vs. opinion?

-Issues: Relevant message selection, Time series smoothing

-Replacement for polls? Promising but not quite yet

Information Contagion: an Empirical Study of the Spread of News on Digg and Twitter Social Networks (Lerman et al.)

Information flow on networks

Dynamics of Social Information

-How does infromation spread on online social networks?

–How far and how fast does information flow on networks?

–What factors influence its spread?

–How does network structure affect dynamics of information flow?

–What does this tell us about quality of information?

-Study question through comparative empirical analysis of 2 social news networks – using URLs as markers

Social News: Digg, Twitter + Tweetmeme

-Tweetmeme aggregates all tweets and features most retweeted URLs on its front page

Data Scope:

-3.5K digg stories with time submitted, promoted, votes for each story (time of vote, name of voter). 140k active users who voted for at least one stroy, 71k of them following at least one user. 258k links = fan network

-398 most retweeted stories 6/11/09 – 7/3/09, extracted from tweetmeme. Retweets of each story, up to 1k most recent retweets. Follower network of users who retweeted the stories

Questions:

-Usability of social netws – do people use digg, twitter the same way? what effect do differences in user interface have?

-dynamics of social networks – how far does info spread, how fast does it spread, and what are the effects of net strucutre?

Basic terms:

-Submitter  = user who submitted link to story, or user who tweeted link to a story

-Vote = vote on Digg or retweet on Twitter

-Fan = fan on Digg or follower on Twitter

User activity: distribution of fans (Power law on Digg with up to 1e5, power law with bump ~ 10 on Twitter with up to 1e7 users)

User activity: distribution of voting: Power law on Digg and Twitter (with different slopes)

Dynamics of stories: both digg and twitter show exponential growth, but for Digg it is preceded by slow period before story is on front page, both show vote saturation

Popularity distribution of stories shows lognormal fit

Information flow on networks: information spreads on a network as fans (followers) vote for (retweet) stories their friends submit or vote for.

Dynamics of information spread on networks looks very similar to overall dynamics of information spread (evolution of fan votes qualitatively similar to evolution of all votes)

BUT distribution of popularity is different, now shows normal fit. “Inequality of popularity” no longer observed (social influence accounted for?). News spreads farther on Twitter than on Digg.

How far does information spread among submitter’s fans?

-On digg many stories get voted by submitter’s fans, opposite case on Twitter

How fast does info spread on networks?

-Two distinct phases on digg: stories spread faster through network before promotion than afterwards.

-On Twitter, info spreads at constant rate.

Network structure differences: Digg network is denser, more inter-connected than Twitter’s

Summary of results:

-Network structure and info flow

–Digg’s network is denser than Twitter’s: News spreads faster initially through Digg’s network, but it does not spread as far as on Twitter

–Twitter’s network is sparse: Fans unconnected to submitter help spread story

-User interface and information flow:

–Before promotion, Digg stories spread mainly through network (and do so faster)

–No equivalent of promotion on Twitter

Tweeting from the Town Square: measuring Geographic Local Networks (Yardi and boyd)

Two geographically bounded events: Wichita shooting and Altanta parking garage collapse

Methods: two crawls and a poll

RQ1: Do geographically local topics have more dense Twitter networks than non-local topics?

Why this is important? People living in close geo proximity may share characteristics. Connecting similar people can help them form ties, foster community

Spread of News

Spread of News Online – ongoing discussion vs. spikes of short-term high-density discussions around real-world events

Distance

Methods: searched key terms about each evenet, stored user info, crawled first degree net of users. Polled users who had tweeted twice or more about church shooting in first 24 hours after it was announced. Administered poll 3-5 days after event. Sent out 800 requests, received 164 responses.

RQ2: Are people who are central in twitter network more geographically central in physical world?

Sarita Yardi gives shout-out to NodeXL, asks for more scale!

RQ3: What sources do people go to for local news events?

Twitter maps show high level of locality to event, slow spread outward

News Sources – go to locals

News Seekers – also go to locals, then to MSM

Practical applications:

-Utilize local short paths for disseminating information. Schools have long used an “emergency phone tree” with specified # of branches and leaves

-Timely notification of unexpected events

Invited Panel: US Government and Social Media

Macon Phillips, Director of New Media for the Obama White House

Moving from Elections to Governance

Wants academics to build tools that show effect of using social media on user behavior

WH new media director Macon Phillips asks for tools that allow thousands of people to communicate with the President (thanks @sadatshami !)

Don Burke, CIA Directorate of Science and Technology, Intellipedia Project

Haym Hirsh, Director, Division of Information and Intelligent Systems

Social Media and the Federal Government

NSF

US Gov’t early crowdsourcing project – National Weather Service Cooperative Observer Program (1890)

-Experimentation:

–CIA Intellipedia

–NASA Clickworkers

–PeerToPatent

–DARPA Balloon Challenge

–EPA Greenversations

–Over 100 gov’t blogs

-Policy implications and clarifications

–70% of Airmen use YouTube

Challenges:

-Legal and Policy

–Terms of Service: Indemnification, etc.

–Advertising (e.g. alongside gov’t content)

–Procurement: Free = Gift? No competition? Charges imposed after lock-in

Additional Challenges:

-Colbert “attacks”

-Open Government Dialogue

The Open Dialogue Top 5:

1. Concerns about Obama’s Birth Certificate

2. Government spending

3. Marijuana

4. Marijuana

5. Birth Certificate

Additional Opportunities: “No matter who you are, most of the smartest people work for someone else.”

Implications:

-Foster experimentation and innovation w/in federal government

-Provide data for innovation outside the def

-align legal and policy with aspirations

-research

Question about contribution quality: do people feel their contributions are worthwhile? How do we make the value and implications of contribution clear?

What do “votes” for questions mean? Who is the right person to say that legalization of marijuana is not a big question? What questions are “big enough to matter”? The “pothole problem” – should questions about fixing potholes be crowdsourced?

Few poorly worded questions about marijuana, people will speak eloquently and argue for the issue, so it’s not just spam

Don Burke – not EVERY system has to be based on socialmedia

Questions: How do you get recognized by gov’t? Answers: open access, publishing where you’ll be noticed

Question about Intellipedia and procedures for aggregating information. Answer: without the wiki, there was no way to share tacit knowledge. But want to go beyond wiki and to the larger web

Jure Leskovec about developing APIs for gov’t data. Answer: no APIs yet, but government is collecting data in one place that’s publicly visible. Want to see scientific community analyzing datasets and finding results, government may not necessarily know what’s a “good” dataset.

***Analysis of Social Network Usage***

Governance in Social Media: A Case Study of the Wikipedia Promotion Process (Leskovec et al.)

Wikipedia promotion process

3 important features:

-deliberative process yielding a single decision

-is publicly recorder

-consequential for the community

Similarity to offline world: people evaluate other people

We study perspective of voters:

-Burke & Kraut examine candidate’s perspective

-How voters evaluate candidate?

-How do evaluations change over time?

Main findings: Relative assessment

-Voter’s evaluation of the candidate reflects different types of relative assessment

–Let voter V vote on candidate C

–we find that vote of V heavily depends on relationship and relative merit of V and C:

—past interaction

—Number of edits

—Number of “barnstars”

–Response function of vote V:

—Prob. V votes given that x other people have voted

Dataset: Wikipedia voting

-Votes are time stamped and signed by users

–2.8k elections sept ’04 – Jan ’08. 44.6% success rate: Successful: 94.7% support. Failed: 31% support votes

–114K votes (78% support). Each vote can get commented: Support votes: 7% get discussed. Oppose votes: 82% get discussed

User characteristcs

-8.3K users voted

–7.5K voters

–2.5k candidates (some go for promotion multiple times)

-Relative merit:

–How do properties of voter V and candidate C affect V’s vote?

–Two natural (but competing) hypotheses:

H1. Prob. that C receives positive vote depends primarily on characteristics of C, there are objective criteria for user to become admin

H2. Prob. that C receives positive vote depends on relationship between characteristics of C and V

Merit (level of contribution):

-Two ways to quantify merit: total #edits, total #barnstars

-Relative merit: How does prob of V voting positively depend on diff in merit of C and V?

Relative merit hypothesis: if V has higher merit than C then he is less likely to vote

Observations: V is especially unlikely to vote for candidates of the same merit (total edits or barnstars)

Direct V-C interaction: Prob of positive vote as function of prior interactions of V and C.

Observation = prior interaction increases probability of a positive vote (with diminishing returns)

Thresholds and diversity of voters:

-Aggregate response function:

–How does prob. of voting positively depend on frac. of positive votes so far?

-Aggregate response function: baseline: if voter were to flip a coin then f(x)=x

-Observation: voters more inclined to express opinion when it goes against prevailing opinion

-Personal response functions: How does prob. of voter V voting positively depend on frac. of positive votes so far?

-Enough data that we can build models of individuals

-Average is close to baseline but individual variation in shape of response function is large

-Over time voters become more conservative, response functions shift downward and to the left

Elections over time:

–Elections unfold over time: Sequence of pairs (s(t),o(t))

—Very negative elections end ealry

—Failed elections are “top-heavy” = start very positive and slowly get negative.

—Successful elections get more positive over time

—Order of early votes doesn’t matter

–False hypotheses: Candidate’s friends vote early, Herding behavior (excessive influence of first votes)

Activity Lifespan: an Analysis of User Survival Patterns in Online Knowledge Sharing Communities (Yang et al.)

-User survival analysis to show that participation patterns and performance factors can account for a considerable amount of variance in predicting user lifespan

-Compare 3 major Q&A sites: Yahoo! Answers, Baidu Knows, and Naver Knowledge-iN

-Discuss how systems might sustain users

-Characteristics of Q&A sites we studied: in Yahoo Answers, earn points at flat rate per answer / best answer, Pay flat rate in points. In Baidu and Naver, earn points at flat rate per answer + points per best answer, and asker can offer additional points

-In Yahoo, significantly more questions / answer

Method: survival analysis

Defining “death” in online communities: period of inactivity exceeding 100 days. Found model prediction not sensitive to different cutoffs (50-150 days)

General comparison: 30-70% users leave after first day, afterwards curves for all 3 sites flatten. YA users more likely to remain than users of other two sites.

Answering life on average longer than asking life across all sites.

Preference between answering and asking (A/R ratio) can account for considerable amount of variance in predicting user lifespan

Initial interaction:

Obtaining more answers to your first question, writing longer question correlated with longer lifespan on Yahoo and Baidu

Winning best answer also correlated with longer lifespan

First 30 days:

More activity, asking more questions, obtaining more answers per question positively correlated with lifespan on all 3 sites

A/R ratio negatively correlated with lifespan on Yahoo but positively correlated with lifespan on Baidu and Naivr

Winning (best answer) also positively correlated with lifespan on all three sites

Analysis: community evolution

All three sites presented a decline in survival rate from year 1 to year 2, especially for Yahoo Answers

Naivr suffered more difficulty in sustaining users in 2nd year as almost no users stayed after 250 days

Conversational vs. Informational: There is a significant and consistence difference in survival patterns between conversational categories and informational categories: more conversational categories survive for longer

*with the exception* of “computer/internet” on Baidu only (cultural difference?)

Analysis: why do YA users stay longer

Fourth International AAAI Conference on Weblogs and Social Media (ICWSM-10)

We will be liveblogging (when possible) from ICWSM 2010, going on now!

Keynote: Bob Kraut, CMU

kraut keynote
implications for community design-offline theories of socialization helpful, not definitive
-online communities can build in good socialization practice-e.g. WP welcoming committee
Two Types of Commitments to Groups-identity based groups-bond based groups
Added Identity & Bond Features to MovieLens
Introduced Subgroups into MovieLens
Identity features that focus on subgroups
Individual profiles
bond-based design:+11% loginsidentity-based design:+44% logins
interventions based on theory increased commitmen
why stronger effect of identity?-time course: social identity can form instantaneously, bonds take time
new approaches to translate theory to design-ABMs
test identity vs. bond design via abm
incorporate design into abm
what are the consequences of discussion moderation-what type of moderation should be imposed? when?
results: indiv moderation helps logins
-personalized mod improves info and social benefit
-comm level mod improves info benefit only in homogeneous communities

***Influence and Composition in Social Networks***

Measuring Influence of Neighbors in Social Networks (Cosley Huttenlocher Kleinberg, Lan Suri)

Measuring Influence in 2 types of socnet data

-Crawl

–provides state of network at specific time

–often have more than one

-Complete time seties

–often from database dump

–provides timestamped history of all events

-This talk: How do measurements of social influence in these two settings compare?

Defining Influence

p(k) = Pr(individual adopts new behavior | k neighbors have)

Analyzing Influence in Wikipedia

-Wikipedia as a social network

–registered users (500K) represented by odes

–communicate through user talk pages

—Definition: Link between u and v forms at time t if one edited the other’s talk page at time t

-Definition: A community is a set of users who edited an article

-Analyze: Pr(user joins a community | k of his friends have)

Ordinal Time Method for Measuring Influence:

-p_o(k) = # instances (over all C) where u had k neighbors in C and joined/  # instances (over all C) where u had k neighbors in C

-advantage: very easy to interpret

-disadvantage: requires complete time series

Snapshot Time Method for Measuring Influence:

-2 snapshots of the network taken at times t1 and t2

p_s(k) = # instances (over all C) where u had k neighbors in C at t_1 and joined before t2 / #instances (over all C) where u had k neighbors in C at t1 and did not join before t1

-advantages: requires only a snapshot

-disadvantage: coarse-grained, don’t know changes in friends between t1 and t2

-results: p_o(k) behaves very differently from p_s(k) on Wikipedia

How are p_o(k) and p_s(k) different?

-accumulation effect in p_o(k) where community joining events can contribute to p_o(1)…p_o(k)

Approximating Ordinal Time from Snapshots

-At each snapshot know:

–which users joined which communities

–how many friends each user had in each community

-Don’t know exact number of friends user had when joined

-Assume a constant rate of getting friends

Ethnicity in Social Networks (Facebook Data Team)

-Questions about how ethnicities engage online are prevalent

-Our goal is to better understand ethnicity on social networks

-Issues

–How do we estimate ethnic distributions on social networks?

–Many services (e.g. Facebook) do not ask their users about their ethnicity

–Our approach infers ethnicities from surnames (based on previous approaches)

Topic modeling approach

-words are names, topics are ethnicities

-Generative process: Draw ethnic bareakdown of aggregate population w/Dirichlet distribution

–For each person draw ethnicity of individiual z_n ~ Multinomial

–Draw surname of individual based on ethnicity ~ Multinomial conditioned on ethnicity

Because there is no FB ground truth, we test our method by scraping 10k users from Myspace

Results: Model does a lot better than naive guessing based on census, internet usage statistics

Also tested on FB data (no ground truth but can have qual results):

1. In 2006, AsAm and White overrepresented on FB, by 2008 ethnicities much closer to mark

2. Drew heatmaps of US, showed that Whites concentrated in the northeast, Asians on the coasts, etc.

3. Correlated with politics: AsAm more likely to be liberal, Whites more likely to be conservative/libertarian

Homophily:

-Strong racial homophily (strongest in Hispanics and Blacks, weakest in Whites)

Homophily by ordinal friendships: racial homophily is strongest for the first few friendships a person makes

Friend you communicate with the most is most likely to be homophilous friend

Who makes friends in social media and why? Rich get Richer vs Seek and Ye Shall Find (Zeynep)

Importance of friendship and social networks vs. debate about social isolation

Architecture of our Commons

-Sociality does not happen in a vacuum

-Mediated Relationships (through digital means)

–Are these relationships: Superficial, weak, fake? Supplementary to offline sociality? As real or more?

–Why does this debate refuse to die?

-Assumptions:

–Internet communication: Anonymous, text-based, fleeting

–Lack of visual cues

but today’s Internet:

–social media: non-transitory interactions, lots of visual cues

Two dominant theories: Rich get Richer vs. Seek and Ye Shall Find

Data: Sample:

-College students

-617 respondents

-multiple classrooms

-very diverse school

-diverse majors

Results: feeling about whether online friendships are possible vs. not possible – split down the middle

Qualitative Component: why do respondents feel online friendships possible / not possible

*Not Possible reasons:

-trust

-face-to-face

-body language and mannerisms

-shared emotions and experiences

*Possible:

-deeper connections – easier (“it may even be easier online as it is all dialogue and no physical characteristics involved”)

-deeper connections – judgment

-bonding is possible

-conversation as key

-experience

***Diffusion and Dynamics in Networks***

Social Dynamics of Activity in a Virtual World (Bakshy et al.)

Second Life paper

Second Life: persistent, immersive virtual world

-User driven objects, economy, society

RQs: 1. How much economic activity occurs in virtual world?

2. Role of groups?

3. How much virtual interaction?

Data:

a) 65 mln user-user transactions (virtual goods)

b) Buddy graph – 4.2 million users, 43 million relationships, focus on strong edges: reciprocal and permissive (can see each other’s online status)

c) chat between 14 million pairs of users

d) 520k groups, 23 million user/group memberships

Economic Activity in Second Life

-Same “sectors” as realworld: retail, real estate, entertainment

-29 million free transactions, 36 million paid, power-law distribution of exchange amounts

Seller in detail: shows that interaction does not necessarily correspond to exchange

Analysis hints at Seller roles? Profiles?

Social Adoption

-show how friends of friends of initial buyers go on to buy as well. Is it influence or homophily? Not sure.

Role of Social ties

-39% of free transactions (but only 7% of paid) were between friends

-40% of users that chat exchange free items, 12% of users that chat engage in paid transactions

Free transactions may be more representative of social activity than paid transasctions

Role of Groups

-Groups indicate aspect of user interest

-Co-grouped transactions

-Sellers more likely to be connected to buyers through co-group than through friendship

Long discussion of why groups are proxy for connections

Explaining Seller Success

Traditional success measures: revenue, repeat business. Regression analysis

Key predictors:

For Revenue:

-Amount made by friends (homophily? business partnerships?)

-Make connections

-Less chatting with customers

-The younger crowd (newer to 2nd Life)

For Repeat Business:

-Interaction!

–Sharing a group, chatting

-More established crowd

-More diverse buyer base

Your Brain on Facebook (Fisher and Counts)

EEG: social vs. traditional media

Why do this? Possible input/feedback to social interaction systems. Short term: Inform design through better understanding of automatic info processing

EEG very good at detecting semantic mismatch

RQs: is Myspace associated with frivolity? Is connecting with friends on Facebook connected with feelings of intimacy?

Media and concepts:

-Media: TV, Books, Social, News

-Concepts: addictive, story, interesting, frivolous, personal, useful

-Showed both front page of FB and personal FB page (asked study participants to briefly friend study on Facebook)

Measurement and Method:

1. Timed, binary decision (low conscious reasoning)

2. Lijkert scale untimed survey (high conscious reasoning)

-Computer-based task: 24 combinations x 22 trials = 528 trials

-16 participants

Results (survey): FB more addictive, but less useful than news. Tells less of a story than books or TV. More frivolous than books, news. More personal than all other forms of media.

Results (decision taks): FB very addictive, tells less of a story than books or TV, a lot more personal than other forms of media. Overall, quite similar questionnaire responses.

Viz: heatmap of head. Time chart. In time chart, look for potential drop ~400 ms after stimulus. The more similar the media to the concept, the bigger the drop.

Results (eeg): FB equal to other media in terms of addictive, interesting, useful, frivolous. Tells less of a story than other media. Is less personal than other media.

Implications, suggestions, limitations

Media parity: FB as interesting, useful, addictive and not frivolous as other media

Personalization and self-identification: Purposeful connection building; easy switching to close friends, family; identity vs. bond attachment

Form of media found most personal by eeg? Books!

Telling stories – status updates on storylines?

Making it tangible: What’s the ratty, marked-up favorite book equivalent in social media? Photo album equivalent?

Limitations: only one form of social networking / media, limited subject population, can we believe EEG results?

Opportunities: compare online media, other subject populations, corroborate with other physiology, expand into real-time capture of physiology

Social Causality and Analysis of Interpersonal Relationships in Online Blogs and Forums (Roxana Girju)

Social causality: causal reasoning used by intelligent agents in a social environment

Modeling social causality – can guide conversation strategies, facilitate modeling and understanding of social emotions, bring new insights

Our focus: social causality as capture through analysis of interpersonal relations in social media

-Pervasive set of english reciprocal textual contexts encoding interpersonal relationships

-data: 11K reciprocal relationship contexts coded

Reciprocity in Language: “The Golden Rule”

Modeling relationships

Properties of interpersonal verbs and reciprocal instances: Symmetry, Affective Value (4-state HMM), Intentionality of Actions

-Intentionality and affective values of interpersonal verbs highly correlated with blame and responsibility

Analyzing Social Interactions:

-Overall 54.34% of dataset was encoded by ambiguous symmetric patterns

-top frequent verb pairs: need-need, love-love

-followed by: hate-hate, miss-miss…

-In general we love people who love/understand/care/need us

-Gender analysis: men initiate more often than females, retaliate more often than women, are more violent and aggressive (whereas women are more forgiving), but this depends on class of verb

-Men and women generally mutually respectful, it is only when respect is broken that responses may differ (e.g. women: cheat -> hate, despise, sue. men: cheat -> dump, divorce)

-Intentionality of actions: intentionality much more often perceived as intentional in bad-bad exchanges than in good-good exchanges

-Reciprocity Chains: Dyadic (formed between 2 people of the form A v B -> B v A -> A v B). Very useful in micro-levle social interaction analysis. General (between multiple people)

In generic chains: retaliation with increased magnitude chains, good for good chains (short), good for bad chains (turn the other cheek)

How does data sampling strategy impact discovery of information diffusion in social media? (Munmun)

How can we sample social web?

Many different modes of social interaction

Scale of interest – viral marketing, ad campaigns

Is there more in social media than just scale?

Social media can have enormous power, e.g. for diffusion

However, inference of such processes is based on the quality of data

Current methods: random walk, snowball – captures structure but not content or context

RQ1: what is role of context in sampling social phenomena, RQ2: how much should we sample to capture the process

Data: Twitter. Look at diffusion via RT feature, shared URl, same hashtag

Model: Diffusion series. Has slots of individuals involved in diffusion process, links between individuals based on relationships

Sampling strategies: given N, the number of nodes to pick, topic T, social graph G

Ignoring social graph: 1. Random sampling from seeds, 2. Attribute / context sampling.

Using social graph: Forest fire (again, random or attribute)

Diffusion Saturation Metrics: user-based (volume, participation, dissemination). Topology-based: (Reach, spread, cascade instances, collection size), Time-based: rate

Our sample S distorts some metric M

Diffusion response metrics: correlate diffusion on twitter to external behavior (search and news trends)

Experimental Study

Reference Set: ~465K users, 836K edges, 30M tweets. 125 randomly chosen “trending topics” from Twitter between Oct and Nov 2009

Trending topic – theme association

Results: bias due to sampling consistent, best results come out of forest fire. For search trends, forest fire + location performs best, for news trends, forest fire + activity performs best

Larger-scale analysis: look at topic distribution by sampling strategy

Inferences about social data affected by sampling strategy. Topic + topology + seed attribute makes a difference to sampling.

Photo Tagging over Time: A Longtitudinal Study of the Role of Attention, Network Density and Motivations (Paul Russo)

Tagging over time

Overview: what factors influence users to tag?

RQs:

1. How do individual motivations affect tagging,

2. what effect does receiving attention from users affect tagging tenure

Data: Flickr – focused on established users

Individual Motivations:

-People tag for themselves (archiving, retreival) and for others (describing)

-Many ways to receive attention from others

-Huberman, Romeru, Wu demonstrated that on YuTube people whose work received more views tended to post more videos, leading to a submission cycle

-Lento et al. photo tagging behavior

New: look at network strucutre (clustering coefficient)

-Mutual Friends

H1: attention, enjoyment, commitment should increase tagging. Density should decrease tagging. ((blogger comment: what about social grooming?))

Method: 90 days of tagging on Flickr, used only “pro” users w/3 months tenure

-Combines user-reported (survey) data and system data: what people say and what they do

-Attention: comments, Density = network, motivation = survey, measured on Likert scale.

-DV = #tags/photo over 90 days

-Results: generally bear out hypotheses.

-Interesting: low density net individuals tag more than high density for same lev of attention, but higher levels of attention lead to more tagging regardless of density. On the other hand, density has positive slope w.r.t. to commitment and tagging only for low density networks, for high density networks higher levels of attention lead to less tagging for high density.

***Microblogging***

Microblogging Inside and Outside the Workplace (Ehrlich and Shami)

Method: Twitter vs. Bluetwit (internal tool)

Data: 34 users from 15 countries and 8 business units that used both Twitter and Bluetwit and 20 posts in each over 4 month period

-Twitter much more active than Bluetwit

Dataset: data collected for BlueTwit and Twitter over 4 month period. 19K posts in two tools. Extracted 4 weeks of tweets.

Manually coded 5k microblogging posts

codes: status, providing information, retweet, ask question, directed, directed q

Results:

Categories of Posts: Most frequent use – provide information, directed posts

Internal use: ask questions, directed posts, style is work-oriented

External use: provide information, style is more “social”

Public vs. Private: clear sense of what is appropriate for an internal-only audience

Reputation Management: Internal: importance of giving back

External: Publicity and Promotion

Fostering Connections: developing better awareness of professional connections in advance of a future planned or unplanned meeting

Consumption of microblogs:

-Microblogs provide early access to human selected information

Discussion:

-How is microblogging useful in a work context?

–Form of crowd-sourcing – asking questions and providing answers

–Anticipatory connections

-What differs between public and private?

–Confidentiality (very clear)

–Style of writing

–Awareness of audience knowledge and interests

–Some erosion of boundaries – despite difference lots overlap

-Why are people consuming microblogs?

–Motivation for posting – Building reputation, awareness

–Motivation for reading – early, quality news

Measuring Influence in Twitter: the Million Follower Fallacy (Cha et al.)

Goal: characterize influence in social media and study its dynamics

1. How can we measure influence of a single user?

2. Does influence of user hold across topics?

3. What behaviors make ordinary users influential?

Data: Twitter

Why Twitter? One of most popular social media, social links are primary way how information flows, traditional media soruces and word-of-mouth coexist in this environment

-54m users, 2B follow links, 1.7B links

–8.5% of profiles private

–95% users belong to the giant component

–low reciprocity (10%)

–Power law degree distribution w/extremely large hubs (500 users have more than 100K followers)

–Low tweeting activity in general (only 11% of all users posted at least 10 tweets)

Three measures of influence:

1. Indegree

2. Mentions

3. Retweets

Are these three measures related? Compared relative ranks of user across 3 measures using spearman’s rank corellation

Correlated for full population, but not for top 10% or top 1% (but retweets corr mention remains high)

top list of indegree = mix of news outlets and public figures, top retweets = celebrities, etc.

Million follower fallacy – Britney Spears has millions of followers but she doesn’t show up on top retweeted list

Measuring influence:

–Find users engage in multiple topics. Picked 3 popular topics in 2009 over 2 month period, iran election, death of  MJ, swine flu

–Focused on 13k people who talked about all 3 topics

Conclusion: just because you have a lot of followers doesn’t mean they retweet you

Along a similar idea, Tweets vs. followers in NodeXL:

Directed Closure Process in Hybrid Social-Information Networks w/Focus on Link Formation on Twitter (Romero and Kleinberg)

How do directed closed triads form?

Triadic closure vs. directed closure:

-Triadic: an edges connects 2 nodes who already have a common neighbor

-Directed: a node A links to node C to which it already has a 2-step path (through node B)

-An edge in directed graph exhibits closure if it completes a 2-step path

-Closure ratio of node C is the fraction of C’s incoming edges that exhibit closure

-could indicates how many nodes discovered C by following nodes that follow C

Data: random sample of 18 twiter micro-celebrities: Users with between 10K and 50K followers

Notation: user A is k-linked to C if A follows C and also follows k followers of C. Let s_k(C) denote set of followers k-linked to C. f(s_k(C)) = fraction of set whose edge to C exhibits closure.

Q1: Is directed closure a significant process? Randomization test

A: up to large k, f(s_k(C)) is significantly bigger than one would expect under random ordering

Observation: f(s_K(C)) increases with k, but flattens out. Why??

Properties observed: closure ratio saturates to a positive constant f, constant f is different for different micro-celebrities, constant f not closely related to total in-degree of micro-celebrity

Heuristic calculation suggests that the sum of in-degrees of incoming nodes closely predicts closure ratio

Improved Model: predict closure ratio not just by in-degree but by sum of in-degree of incoming nodes. Also sum of in-degrees of incoming nodes from same community predicts closure ratio even better!

Conclusion: definition and methodology for directed closure, evidence for directed closure on Twitter, evidence that sum of in-degrees of incoming nodes & nodes from same community predicts closure ratio on Twitter

Characterizing Microblogs with Topic Models (Ramage, Dumais and Liebling)

Do people like the posts they see?

43 users at MSR looked at 60 posts, judged as: “not really worth reading <-> maybe worth time spent reading <-> worth the time spent reading.”

the average judgment was maybe or worse, nobody on average judged posts as worth time

Fundamental problem: people followed <> Tweets worth reading

What factors go into deciding if a user is worth following?

Method: Structured interviews with heavy users, Broader survey of 56 Twitter users

Kinds of Topics for Tweets. Hobbies, professions, news, products, events = Substance. Updates about meals, travel, hygiene = status. Making plans, networking, staying in touch = Social. Humor, wit, whininess, diction, worldview = Style. Different topics liked by different users.

Shows that content is important.

Content modeling: 8.2m “Spritzer” Tweets from 2008

-Surface word features = tf idf cosine similarity, etc.

-won’t look at deeper features (e.g. parsing) – not very appropriate for Tweets

-Desparsify: Topic Models, LDA.

-Want some labels (hashtags, emoticons, questions, etc.) = Naive Bayes, SVM, etc.

-Combined surface word features, LDA, labels = labeled LDA

Content modeling with labeled LDA:

1. Discover unlabeled topics w/ k=200 latent topic dimensions (e.g. politics, sleep)

2. model common labels = 500-1000 dimensions for hashtags, emoticons, etc.

Twitter content by category: manually aggregate topics into one or more of 45 categories.

Results: 38% style, 23% social, 27% substance, 12% status

Treemap-like visualization

Filtering: Tweet stream re-ranking

Split rater’s post into train 70% and test 30%

re-rank test set by distance to positive examples

consider judgment of maybe or worth time as “positive”

Mean reciprocal Rank @ 1 Relevant: best performance = Labeled LDA + tf-idf, .75

Finding: User recommendation task

Rater’s followed user: train 6/7 followers and test 1/7. Find the test user among 8 other non-followed users. Ranking task: score by reciprocal rank of test user. Performance > .9

Next steps: better interfaces for finding and filtering and models that account for temporal dynamics

Fourth International AAAI Conference on Weblogs and Social Media
(ICWSM-10)
May 23-26, 2010
George Washington University, Washington, DC

Sponsored by the Association for the Advancement of Artificial Intelligence

The ICWSM 2010 conference starts Sunday.  This is a very high quality conference on the study of social media.  My colleague, Professor Derek Hansen, and I will lead a tutorial on using NodeXL to analyze social media networks.

SA2: Introduction to Social Media Network Analysis
Marc Smith (Connected Action) and
Derek Hansen (University of Maryland)

Social networks are the defining data structure of social media, created as people reply, link, click, favorite, friend, re-tweet, co-edit, mention, or tag one another. In this tutorial, we review the core concepts and methods of social network analysis and apply it to the collection, analysis, and visualization of social media networks. Using the free and open NodeXL application, learn how to extract a social media network and generate metrics and visualizations that highlight key people and positions within streams of tweets, videos, photos, or emails.

Fourth International AAAI Conference on Weblogs and Social Media (ICWSM-10)
May 23-26, 2010
George Washington University, Washington, DC

Sponsored by the Association for the Advancement of Artificial Intelligence

IMPORTANT DATES:
Tutorial Proposals: December 1, 2009
Paper Submission: January 8, 2010
Poster/Demo Submission: January 8, 2010
Paper Acceptance: March 3, 2010
Poster/Demo Acceptance: March 3, 2010
Workshop Submission: March 1, 2010
Camera Ready Copies: March 12, 2010

Featuring a keynote by:
Professor Bob Kraut, CMU,
on “Designing Online Communities from Theory”

Professor Michael Kearns, Computer and Information Science,
Univ. of Pennsylvania,
on “Behavioral Experiments in Strategic Networks”

Speakers in Special Sessions:
- Nicole Ellison, Dept. of Telecommunication,
Information Studies and Media, Michigan State Univ.
- James Pennebaker, Dept. of Psychology, Univ. of Texas, Austin
- S. Craig Watkins, Dept. of Radio, TV and Film, Univ. of Texas, Austin- Don Burke, CIA Directorate of Science and Technology, Intellipedia
- Haym Hirsh, National Science Foundation IIS Division Director
- Macon Phillips, U.S. White House, Head of New Media

Tutorial Speakers will include:
- Jake Hofman, Yahoo! Research,
“Large-scale social media analytics with Hadoop”

- Cindy Chung and James Pennebaker, Univ. Texas,
“Using LIWC to uncover social psychology in social media”

TECHNICAL AREAS
More specifically, ICWSM welcomes submissions from researchers in a number of disciplines:
- Computational Linguistics/NLP
- Text Mining/Data Mining/Machine Learning
- Psychology
- Sociology (including Social Network Analysis)
- Anthropology, Communications, Media Studies
- Visualization
- HCI
- Graph theory, concrete analysis and simulation of graphical models

Submissions are welcome that study a broad array of types social data, including:
- Weblogs, including comments
- Social Networking Sites
- Microblogs
- Wikis (wikipedia)
- Forums, usenet
- Community media sites: youtube, flickr

Technical topics of interest include:
- Psychological, personality-based and ethnographic studies of social media
- Analyzing the relationship between social media and mainstream media
- Qualitative and quantitative studies of social media
- Centrality/influence of social media publications and authors
- Ranking/relevance of blogs; web page ranking based on blogs
- Social network analysis; communities identification; expertise and authority discovery; collaborative filtering
- Trust; reputation; recommendation systems
- Human computer interaction; social media tools; navigation and visualization
- Subjectivity in textual data; sentiment analysis; polarity/opinion identification and extraction
- Text categorization; topic recognition; demographic/gender/age identification
- Trend identification and tracking; time series forecasting; measuring predictability of phenomena based on social media
- New social media applications; interfaces; interaction techniques

SUBMISSION
People interested in participating should submit through the ICWSM-10 website a technical paper (up to 8 pages, not including references),poster or demo description (up to 4 pages) by the deadlines given above (Midnight PST). Papers must be must be formatted in AAAI two-column, camera-ready style (see the AAAI author instructions page at http://www.aaai.org/Publications/Author/author.php). Details for the submission procedure will appear at the conference website: http://icwsm.org

PUBLICATION
All accepted papers and abstracts will be allocated eight (8) pages in the conference proceedings. Authors will be required to transfer copyright of their paper to AAAI.

DATA CHALLENGE
ICWSM-10 will once again hold a data challenge featuring a freely-available dataset and a half-day workshop at the conference. Details will be posted on the conference website.

CONFERENCE WEBSITE
www.icwsm.org

For general information regarding ICWSM-10, please write to icws…@aaai.org. More details about the CFP and the conference will appear on the website over time.

ORGANIZERS:
Program Chairs:
William Cohen, CMU Computer Science
Samuel Gosling, U Texas Dept of Psychology

General Chair:
Marti Hearst, UC Berkeley School of Information

Senior Program Committee Members:
Lada Adamic, Univ. of Michigan
danah boyd, Microsoft Research
Claire Cardie, Cornell Univ.
Kathleen Carley, Carnegie Mellon Univ.
Chris Diehl, Lawrence Livermore National Labs
Nicole Ellison, Dept of Telecommunication, Information Studies, Michigan State University
Lise Getoor, Univ. of Maryland
Jure Leskovec, Stanford Univ.
Winter Mason, Yahoo! Research
Kate Neiderhoffer, Dachis Corporation
Eugene Agichtein, Emory Univ.
Cindy Chung, Univ. of Texas at Austin
Scott Counts, Microsoft Research
Tim Finin, UMBC
Evgeniy Gabrilovich, Yahoo! Research
Kristina Lerman, ISI-USC
Jure Leskovec
Winter Mason
Gilad Mishne, Yahoo! Labs
Bo Pang, Yahoo! Research
Marc Smith, Connected Action Consulting Group

Some highlights:
An Examination of Language Use in Online Dating Profiles
Meenakshi Nagarajan, Marti Hearst

Event Detection and Tracking in Social Streams
Hassan Sayyadi, Matthew Hurst, Alexey Maykov

Gephi: An Open Source Software for Exploring and Manipulating Networks
Mathieu Bastian, Sebastian Heymann, Mathieu Jacomy

Information Diffusion in Computer Science Citation Networks
Xiaolin Shi, Belle Tseng, Lada Adamic

Considering the Sources: Comparing Linking Patterns in Usenet and Blogs
Mary McGlohon, Matthew Hurst

[Vladimir Barash is liveblogging the ICWSM conference]

10.30am A categorical model for discovering latent structure in social annotations (Said Kashoob)
Given a collection of web objects, users and tags, can we model the underlying tag generation process?

-Discover implict communities of interest?

-Categories of related tags?

-For given category, id most relevant objs for category

-compare categories

Initial thoughts: content-based topic modeling (Latent Dirichlet Allocation, LSA). Recent work applying LDA models to tags (Wu 2006, Zhou 2008)

Modeling social annotations: the process that generates content is fundamentally different from the annotation process (many authors per “document” = tag collection, not aware of each other)

Community based categorical annotation model (CCA). Communities are groups forming around interests, etc. Each community has a number of categories as its world-view. For each object, a community draws tags from the appropriate underlying categories

Object annotations are generated by communities. Each community selects tags from its category set.

-use Gibbs sampling to recover a joint distribution of tags, categories and communities

-can do inference to find most likely tags per category, per community

Content-based topics vs. tag-based categories

Exploring content vs. annotation: for pairs of objects that are similar in category space, how topically similar are they? Reslt: objects with similar content do not necessarily have similar tags and vice versa

Rubix cube example: objects similar both in category and topic are solutions to the puzzle, objects that are only similar in category are puzzles / games, objects that are only similar in topic are math pages

11am Content-based summarization and categorization in the blogosphere (Ahmed Hassan)

How can we decide which blogs are more important / influential? Given a set of blogs related to a particular topic, find a subset of blog feeds to read that have continued interest in the topic.

Can we use hyperlink popularity based algorithms for speeches and blogs? Yes, but they might not work very well

Use textual similarity to link posts instead of hyperlinks: maybe blog A affects blog B? Given a set of blogs, build a graph where nodes represent posts/feeds and edges link posts/feed with simliar text

Use a pagerank-like measure to calculate importance score of a blog in the similarity network

How can we select nodes that are important but diverse? Add discounting factor based on similarity of node to neighbors

dataset: TREC blog datase

Evaluation: use linear threshold diffusion model! How many blogs covered (activated) by first k blogs in rank. Also split data by time to see how valid is rank(t) for predicting coverage at t+1. Approach also does a little better at precision-at-k on the TREC blog dataset

11.30am Supervised ranking of syntactic configurations for finding targets of semantic expressions (Jason Kessler)

Trying to find targets of sentiment phrases (“while the dealership was friendly, the *car* was a disappointment”)

Sentiment expressions only link to physical targets (“Bill likes to drive the car”)

Two-domain corpus: cars and camera

Baselines – proximity, one-hop with dependency parser

Approach – learn to target from a corpus. Supervised ranking instead of classification. Uses linear-kernel RankSVM, off-the-shelf approach

Results – supervised ranking does better than proximity, one-hop, approaches interannotator agreement

Future work: inter-sentential target

3pm Stochastic models of user-contributory web sites (Tad Hogg)

Focus: describe aggregate group behavior

-determines structure and usefulness of user-participatory sites

Models enable:

-predicting user behaviors

-incentivizing user participation

Stochastic modeling summary:

-Start with individual user behavior, specify states and transitions between states

-Determine collective behavior (details in paper)

Illustration: Stochastic Model of Digg

-Phenomenology: users submit and vote on news stories, Digg promotes popular stories to front page, allows social networking (friends, fans)

Model of Digg voting behavior: visibility and interestingness -> votes. Extension to prior model (Lerman ’07).

- “law of surfing” for viewing web pages (Huberman et al. 98)

- incremental average growth in number of voters’ fans

- construct equation for dynamics of vote volume for a story from state diagram that formalizes visibility and interestingness. Params for vis and interest estimated from story sample. Estimate viewers watching stories from models and data.

Data: front page and upcoming stories since May 06

Modeling story visibility: story location, navigating web sites, number of fans. Each voter enables fans to see story via friends interface.

Modeling story interestingness: topic, novelty, popularity. Can estimate from web-based experiments, e.g. Salganik et al. 06, but can estimate from models and data.

Results: model captures qualitative features – slow growth initially, influence of fans on promotion, rapid growth if story promoted (much more visible to users)

Results: the number of fans have not yet seen the story drops, number of votes on story grows significantly after story gets promoted. “Promotion line” in number of fans / interestingness splits stories into will be / won’t be promoted with 95% accuracy

Predictions from early behavior: can predict #votes from first 4 votes (similar to results for YouTube), but “law of surfing” and incremental growth important parts of model

Conclusions: stochastic process approach connects user and system behaviors, applicable to social media in general when users have limited information and actions, limited use of personalized history.

3.30pm Personal information management vs resource sharing: towards a model of information behavior in social tagging systems (Markus Heckner)

Why do people tag?

Tagging: a fourth layer of indexing? (On top of author keywords, intellectual indexing by information professionals, and auto-tagging)

media type influences tagging: differences in number, language, function of tags btw Connotea, Flickr, YouTube, Delicious

Method: Scientific crowdsourcing using Mechanical Turk

Assumption: Different motivations for taggs

-Organization of one’s own digital content, i.e. personal informational management (Delicious, Connotea), vs. information sharing (Flickr, YouTube)

Questionnaire Design: Question Types

-online questionnaire posted as “human intelligence task.” asks general information, general motivation, tagging motivation and understanding, social bookmarking and search, recent usage.

Data: ~150 subjects, users of Flickr, YouTube, Delicious, Connotea

Results:

Motivation: YouTube is significantly weaker-motivated for PIM, Delicious much weaker for sharing, Flickr and Connotea about even

Perception of tagging: Connotea users perceive tagging as most easy, follow by YouTube, Delicious, Flickr (not significant). Connotea user agree very strongly that tagging is a useful feature

Towards a model of tagging behavior: Shneiderman’s approach towards social software (social spheres), etc.

4pm Motivational, structural and tenure factors that impact online community photo sharing (Oded Nov)

Why do people in online communities share? Can we quantify the drivers for sharing (or not sharing) and their effect on actual behavior?

Three types of questions as framework:

Why – drivers of sharing (Motivation, structural properties, personality, privacy concerns)

What – type of information shared (code, content/facts, meta-info (tags), photos)

Where – context of sharing (OSS, Wikipedia, Flickr)

Creation vs. sharing: the act of sharing is separate from the act of creation

People take photos regardless of the sharing act (really?), the “second act” of sharing photos is optional, separate from the “first act” of photo sharing

Identifying the factors in sharing: motivational (extrinsic vs. intrinsic), structural factors (position of user in community network), tenure in community

Motivations: enjoyment (self/intrinsic), commitment to the community (others/intrinsic), self-development (self/extrinsic), reputation (others/extrinsic)

Response variable: artifact sharing per tenure year, IV: motivational vars + structural (number of contacts) + tenure (years since started sharing)

Method: combine user-reported (survey) data and system data: what people say + what people do. N=278, used only “pro” users (>200 photos) with at least 3 months’ tenure on Flickr.

Results: significant positive effect of commitment, negative of self-development, positive of number of contacts, negative of tenure, rest not significant.

Why is enjoyment not correlated with sharing? Users may be motivated more by “fun” of creation rather than content sharing.

Why is correlation between self-development and photo sharing nefative? A tradeoff between contribution quality and quantity? Greater self-development motivation -> focus on the quality of artifacts shared, at the expense of quantity

Summary:

quality / quantity tradeoff, fun is not an issue?, diminishing sharing

4.30pm Modeling Blog Dynamics (Michaela Goetz)

Blogosphere is a system of interactions: Entities: Bloggers, Posts, Topics

Model: simple set of rules (followed by blogger) that creates these interactions

Evaluation: creating a synthetic blogosphere, comparing it to real blogosphere

Motivation: forecasting, advertising

How is this different from modeling social network? 2 networks combined: Blog vs. Post network, complex temporal dynamics

goal: model micro-level interactions to observe macro-level interactions in blogosphere

Properties of the blogosphere:

-Topological – blog, post = follow power law distribution

-Temporal – user posting activity, popularity over time (link creation)

Burstiness (Slope = 1 of aggregation level vs. entropy) & Self-similarity (Linearity of aggregation level vs. entropy)

Inter-posting time follows a power law

Time t vs. number of in-links t days after publishing follows a power law

Desired model: simple (no parameters), intuitive (local rules), creates realistic topology and dynamics

First-try solution:

-inter posting times sampled from exponential distribution, links created using pref attachment – leads to exponential inter-posting distribution, poisson degree distribution (really?), etc.

Second-try solution (Zero-Cost):

In every round, for every blog, user u takes a random walk step, if he reaches 0, he decides to post P

when he posts, he can make a link or not, if he makes a link, he can choose a neighbor based on frequency of links or non-neighbor, then chooses some post of neighbor and links to random posts upward in the cascade

This model accurately reproduces both the topological and temporal patterns (at a qualitative level – same distributions, different though relatively close exponents. Biggest difference: 1.5(sim) vs. 0.7(real) in inter-posting time exponent)

[Vladimir Barash is liveblogging the ICWSM conference]
9-10AM: A Tempest: Or, on the Flood of Interest in Sentiment Analysis, Opinion Mining, and the Computational Treatment of Subjective Language (Lillian Lee)

-Sentiment analysis using discussion structure: clasify speeches in US congressional floor debates as supporting or opposing proposed legislation -Individual doc classifier -agreement (degree) classifier for pairs of speeches

-Agreement info allows COLLECTIVE CLASSIFICATION – “agreeing speeches should get the same label”

-ECON: debate about effect of sentiment on sales
-comScore (users willing to pay 20-99% more for 5 star item vs. 4 star item)
-Jury is still out

-SOC: What opinions are influential? (Niculescu-Danescu Muzyl et al.)
-Prior work has focused on features of text and has not been in context of sociological aspects of reviews
-look at helpfulness scores

-What about review’s star rating in relationship to others?

-theories from soc / social psych:
-conformity
-brilliant but cruel

-Are the social effects just textual correlates?

-would like to control for actual quality of review text. Manual annotation? Tedious, subjective. Automatic clasification? Need extremely high accuracy guarantees.

-use plagiarism (1% of all reviews) to control for text quality! findings hold for plagiarized pairs

Summarizing:

-Sentiment analysis has many important applications – to researchers, to citizens, to governments

-encompasses many interesting research questions

-extends to many areas

Stand-out question: matt hurst and the user as generative model for opinions

10.30 AM: Gesundheit! Modeling Contagion through Facebook News Feed
(Eric Sun, Itamar Rosenn, Cameron Marlow, Thomas Lento)

Motivation: how do ideas diffuse through a large social network?

-Theory of the Influentials (Gladwell)

-Accidental Influencers(Watts): anyone can be an “influencer.” Ideas don’t spread via influentials, ideas spread like viruses (susceptible or not), goal to find a large number of susceptible people

Q: are contagions triggered by small # of sources? What are some characteristics of diffusion chains on Facebook? Can we use demographic or behavioral characteristics to predict size of diffusion chains a particular user will create?

Spreading ideas on Facebook – through News Feed

-Page Fanning = becoming fan of people, orgs, etc.

-Mechanics: Alice fans a page, Bob sees Alice’s action on his News feed, Bob fans page as well (link: Alice -> Bob)

-Strong ties: links depend both on friendship and on actions (following)

-Median page has most of its fans in one (weakly) connected cluster

-Large clusters Not Started by “one guy” – roughly 15% of fans in the biggest cluster of each Page are start points

-Variability in this percentage becomes very small as #fans increases

-Clusters are formed when many short diffusion chains merge

-Data: actor to follower connections for ~300,000 FB paes

-Main dataset: page-level data

-Second dataset: select 10 random, representative pages (at least 40 days old had at least 5k fans) and analyze users that start chains

-Prediction Model: Response = max_chain_length, Predictors: gender, log age, log FB age, etc. Method: 0-inflated neg binomial regression

-results: Demographic characteristics not important, number of Facebook friends not important, feed exposure is the strongest predictor with coefficient ~ 1 (so a 1% increase in the number of people who see ego’s fanning ~ 1% increase in chain length)

-Comment: this is global focus, not local focus. What about the interpersonal dimension, i.e. the likelihood that Alice infects Bob?

-Comment: support for Duncan Watts’ idea

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11am Seeking and Offering Expertise across Categories: A Sustainable Mechanism Works for Baidu Knows
(Jiang Yang, Xiao Wei)

Baidu Knows: Chinese QnA site

-Growing extremely fast: more than 80 million questions asked in 4 years.

-Huge user population (2.6 mln users). Knowledge repository as online source

-Points! Points! Points! (flexible amount of extra points set for best answerer, more points buy more answers, etc.)

-Building sense of community: honor title system (including traditional Chinese titles!), online chats, etc.

-Data: Full history of QnA 12/07-05/08. 9.3 mln questions asked, 5.2 mln (56%) resolve, 2.6 mln users participated

-3.3 answers for each question (vs. 7.3 for Yahoo! Answers, note that Yahoo! Answers encourages answering more than asking)

-Significant categorical difference in awarded points: low(brand, science, food) vs. high (medicine, computer, music)

-Price of answering positively correlated to popularity of category

-Order difference: according to human rating of sample questions, order of answers doesn’t matter, but first answer has highest chance to be best answer, more points awarded for later best answers

-Reinforcement cycle: encourage continuing

-Answerer performance positively correlated with activity level. More active answerers choose less expensive questions, questions with fewer answers. More active answerers working harder (longer answers), and more focused (on particular category)

Reinforcement cycle: choose less competitive q’s -> better performance -> more efforts -> more focused -> choose less competitive q’s

-Askers: learn how to better ask: more active askers, ask cheaper questions, experienced askers get more answers with per point they ask (slight trend).

-Asker/Answerer hybrids (22% of pop): core of contribution! Much more active (almost 1/2 total questions), more generous (offer higher award: 12.3 per question versus 11.6 on average in general, share same pattern as normal asker but paying higher each time), not necessarily experts, incentivized

Seeking and offering across categories: some categories are pretty self-contained, others are more porous. Lots of cross-category contribution

-A sustainable mechanism is working on Baidu Knows (that’s a good discussion question!)

11.30 AM: Community Structure and Information Flow in Usenet: Improving Analysis with a Thread Ownership Model (Mary McGlohon, Matthew Hurst)

-Compare communities of online social nets may lend insight into how groups form and thrive

-How does info diffuse between communities?

Data: Usenet, 200 politically-oriented newsgroups (bulletin boards) – polit in name, Jan 04 – june 08. several countries, 19.6 mln unique articles, 6.2 mln cross-posted

Cross-posting: large % of articles are cross-posted to multiple groups. Somebody reading one group may “reply-to-all” such that all groups see it.

Structural analysis: how do edges btw authors form? How does the reciprocity of groups compare? How can we measure similarity btw groups?

-Make network of authors for each group, if a_1 has replied to a_2 at any point, there is an edge from a_1 to a_2. Find power law relationship btw #of nodes and #edges over time (similar to Leskovec et al. densification). Exception: tw.bbs

-Reciprocity: which groups have highest reciprocity? Top 10 were European newsgroups, e.g. hun.politika (up to .58). Lowest reciprocity: tw.bbs

-Similarity: use Jaccard coefficient for cross-posts = #shared articles btw 2 groups / Total # articles in groups, can do same with shared authors

Highest similarity ~.54 (bc.politics and on.politics).

Draw thresholded similarity network, find clusters: parties, US regional, countries, alt.politics subgroups

-Image: english-speaking countries cluster. Can.politics (Canada) highly central!

Ownership Model: we would like to find out in which group the activity is truly occurring. How can we trace this? ANswer: assign “ownership” based on authors of posts. First, assign authors to groups based on devotion, where devotion(a,g): what % of an author a’s posts are exclusively posted to a given group g

-For all groups that author posts particular post p to, the post belongs to the group with the highest (normalized) ownership between it and the author

-Example: “Kiss the National Parks Good-Bye” initially corss-posted to several groups, 38 groups in total, ownership concentrated in seattle.politics and or.politics

Information flow between groups: How often does an author in group 1 respond to a post in group 2? Define influence g_a, g_b as the product of the groups’ devotion scores for a particular author

Ownership-based similarity. Q: How can ownership help us more precisely state when 2 groups are similar? Use devotion instead of Jaccard to calc similarity between groups

-Potential applications: link prediction, IR and relevance, ownership for email lists. Future work: use ownership to predict whether group will continue or die off

1.30pm Does Showing off Help to Make Friends? (Christophe Aguiton)

Self exposition and social capital:

-What do we let others see about ourselves on social networking sites?

-How do we relate to others depending on what they show?

Game sociological survey: link

part 1: if you were on these pics, which would you publish on a website?

part 2: questionnaire

part 3: down-to-one-friend (start with x friends, see pics only, eliminate one; add favorites info, elminate one; etc. down to one friend)

first launch: FB, diffuses to blogs, Flickr, news, 15,000 respondents by end of experiment.

-Sample is not representative of French SNS users. Lots of heavy internet users. 71.1% male, average age 28 years old, 47% high school diploma, 33% students.

First Results:

-moderate / controlled level of exposure (exposure score: ~2.4 on 1-4 scale)

-extraversion index, socializing index

Method: PCA to cluster photos in the experimental dataset. Four components: traditional self-exhibition (ordinary life situation), bodily immodesty (nudity / sexual situations), showing off (protests, etc.), provocative (negative activity).

-Cluster analysis with scores of PCA, five clusters: Modest (people don’t like to show themselves, 19%, more women, older, high level of ed, high status position, few friends) + four from above.

-Main question: find no correlation between sns use and level of self-exposition

-2nd question: how do people make friends?

- popular friendship targets (from 3rd part of game) are young, cool, active, unpopular are  older, more reserved

- subject choice largely guided by homophily, a tendency to bond with similar others. Results: people preferentially choose as friends of same age and diploma level. Heterophily by gender: both men and women choose women over men.

- What aspects of persona do different kinds of people look at? Modest people most closely look at “about me,” traditional exhibitionists most closely look at “wall,” provocative most closely look at “sexual preferences”

Main results of survey:

-Self-exposition on web is a social construction, requires reflexive and strategic control of one’s image, self-exhibition strategies differ according to sociological factors, social networks encourage homophily but also allow users to have more heterogeneous social capital

2pm. What are they blogging about? Personality, topic and motivation in blogs (Alastair J. Gill et al.)

How does personality influence blogger motivation?

Personality – describes fundamental core of individuals

-Behavior and preferences

-Useful for categorising users and consumers

-How does this influence bloggers? Blogs – unique freedom of expression for authors

-Already shown to influence langauge in CMC (Gill 2004, Nowson 2006).

-Analysis of Polish blogs w/ suggested psychological profiles)

Motivations: Internal – Documenting life, catharsis (therapy); External using own perspective – Interests, Opinions

Personality: Big Five model of personality (Goldberg ’92, Costa and McCrae ’92).

Data and Method: Internet meme personality test: 5 Y/N questions each for the Big Five personality types -> high-mid-low scores; 3 months of blogs extracted from Nielsen BuzzMetrics data. Basic statistics, text analysis.

results:

Neuroticism: use of blogs for self-therapy/catharsis – focusing on self and venting purely negative feelings

Extraversion: life narrative (documentation) in conversation with reader; expressing highs and lows, but not mundane. Use of 2nd person pronouns

Openness: review or evaluation of leisure (music, TV) from personal perspective, but no increase in thinking or senses

Conscientiousness: faithfully document life going on; references to others; positive emotion. Job focus, little temporal narrative.

Agreeableness: positive self-talk focus

Discussion: Blogs unsurprising mainly focus on self. Face apparently genuine in blogs. Agreeable bloggers provide a barometer of what is / isn’t acceptable in blogs

2.30pm A social identity approach to identify familiar strangers in a social network (Nitin Agrawal)

Who are familiar strangers?

Observe repeatedly, but do not know each other: Real world – people you see daily on a train (going to same workplace); Blogosphere – people who have similar blogging behavior / interests but not in each other’s social networks

Together, familiar strangers form a critical mass: understanding of one blogger gives a sensible and representative glimpse to others -> better customization, personalization and recommendation.

Familiar strangers in social media: an example, u is a blogger with interests A_u, friends v_1… v_k with interests A_v_1… A_v_k. Find non-adjacent u’ with similar interests (intersection of A_u, A_u’ is non-empty).

-Egocentric network view (exposure to network limited to neighbors).

-Social identity approach: cluster contacts into groups, propagate search through relevant clusters of contacts (prunes search space). For this to work, network needs to be a small world (WS 98)

-Method: represent contact by tag vector, content vector, use cosine similarity, then k-means clustering

-Ground truth: Global network view. Data: Blogcatalog (~24k nodes), DBLP (~35k nodes). Also compare to exhaustive and random search strategies.

Results: 79.3%+-3 for BlogCatalog, 91.3%+-2.1 for DBLP, greatly reduced search space.

3pm You are where you edit: Locating Wikipedia Contributors through Edit histories (Michael Lieberman, Jimmy Lin)

Minig Wikipedia: id Wikipedia contributors who edit geopages in a constrained space, have specific “pet” geopages (pages for geographical locations identified with geotags)

Features with extent: all geopages tagged with single lat/lon, even though they can be countries, cities, rivers, etc.

Wikiepdia edit histories: ignored anon edits, minor edits, focused on edits to geopages

Edit area = convex hull of geotags smaller than 1 degree sq. Account for outliers with simple approximator that cuts off at F closest-together geotags

Results: Pet Geopages. Over 50% of contributors with 5-20 edits, and 25% of contributors with over 20 edits, have 80% of edits to 1 or 2 geopages

Reasons for Tight Edit areas: randomly selected 100 contributors with at least 10 edits to geopages and small edit areas. Concurrently examined contributors’ user pages and the set of edited geopages to determine an interest. Contributors with small edit areas tend to be born in or are living in close-to-edit areas.

Future work: using alterante measures to determine geopage edit significance

4pm CourseRank: a closed-community social system through the magnifying glass(Georgia Koutrika)

CourseRank: community for Stanford students to evaluate courses, browse courses, plan academic program, interact with each other, ask / answer questions. 1.5 years, 11k students, 19k courses, 3k reviews

Special features: well-defined closed community, multiple constituencies (staff, students), special-purpose tools, hybrid data

A new class of social sites defined by these characteristics. E.g. university social site, scientific social site, A-space (intelligence)

Popularity: >85% of Stanford students are CourseRank users

Usage: follows academic cycle

Participation inequality: 20% created by intermittent, 80% by active; 31% of lurkers, 38% intermittent, 30% (!) active

Smaller communities (departments) breed more active students

Truths and Lies: grade distribution follows official. Good incentives make better users (is this really evidence?). But there is bias: correlation between grade given to student and rating given by student

Lessons Learned:

-added-value services a big thing

-high-quality data

-community feeling is strong = students coming together with common need

-meaningful incentives

4.30pm Using transactional information to predict link strength in online social networks (Indika Kahanda)

OSNs (Online Social Networks) are larger and more heterogeneous than manually-collected social networks

High median degree implies presence of many weak links

Conjecture: Link strength can be predicted from transactional information

Data: Purdue FB. Transactional info: Wall comm, photo postings, group memberships. Networks over Wall, Pictures look more like offline-collected networks (e.g. AdHealth data)

Automatically identifying top friends: link strength prediction task (binary)

Related to, but different from, link prediction (which focuses on predicting future links between u,v in a unimodal network). Previous approaches use attribute similarity features or topological features of network. Adamic and Adar (’03) used ancillary networks but focused on similarity vs. transaction

Feature types: Attribute-based (attribute similarity btw two nodes), Topological features (assess connectivity of users in friendship network), transactional features (number of bi-directional wall/photo/group posts), network-transactional features (assess connectivity of users in transaction networks)

Experiment 1: Feature rankings. Compare relative importance of each of 50 features, using info gain and chi-square statistic. 12 of top 15 are network-transactional features, 3 are transactional, 12 use wall info, 3 use picture info.

Experiment 2: Feature type comparison. Ablation study. Network-transactional features achieve best performance

Experiment 3: Link type comparison. Ablation study using data from each link type separately (all features). Wall information results in best performance. Picture info does not improve performance because of sparsity

Experiment 4: overall classification results. Bagged decision trees perform best.

Results indicate that transactional events useful for presenting link strength, but should be used in context of larger network for best performance

5pm RevRank: a fully unsupervised algorithm for selecting the most helpful book reviews (Oren Tsur)

Most reviews are: repetitive, limited contribution, poorly written, unnoticed

User voting bias: Liu et al. – imbalance vote bias, early bird bias, winner circle bias. Many very helpful reviews go unnoticed.

Interesting features of reviews:

-there are a lot of them

-contributors put big cognitive effort to generate them

-Good faith. Reviewers expect no direct reward.

Main idea: automatic detection of dominant concepts. Dominant concepts are either really frequent or infrequent but very informative. Term dominance defined as ratio of term frequency in review set to term frequency in balanced review set (British National Corpus)

RevRank algorithm: find most dominant concept, vectorize, rank reviews according from centroid identified by the core vector

Experimental setup: 12k reviews for Da Vinci Doe, World is Flat, Harry Potter, Ender’s Game. Compared to random, user votes. Gold standard – human labels.

Results: in 85% of test batches, RevRank pick was ranked “the most helpful.” In some cases, random algorithm outperformed user votes!

Summary: RevRank is fully unsupervised, better than user votes, finds “hidden” reviews and interesting insights

End of Day 1

On Tuesday night Marc Smith and I will be presenting the poster for our paper, “Distinguishing Knowledge vs Social Capital in Social Media with Roles and Context” at the International Conference for Weblogs and Social Media. You can find the poster, co-authored with Marc Smith (Telligent Systems), Lise Getoor (University of Maryland) and Howard T. Welser (Ohio University), here. The full text of the paper has more information, but the poster is a good summary of the key concepts in the paper:

• What roles do people play in social media?
• What contexts shape user behavior in social media?
• How can we leverage roles and context together to predict future user behavior (in terms of contribution type) from past user behavior?

The specific research question being addressed is: can we predict whether a particular contribution to Live Q&A (a Microsoft-sponsored community question answering site) will be contain factual information, or discussion / chat. It is possible to do the prediction based on the text of the contribution, but such an approach focuses entirely on the content, and not on the actor – the user who is making the contribution. If we leverage actor-centric information (what role does he/she play in the community: an “answer person” or a “discussion person”? is he making the contribution in a discussion-oriented context, such as implied by tagging the contribution as “fun,” or a fact-oriented context, such as implied by tagging the contribution as “math”?), we find we can build a decent predictor at very low cost with very few variables. If we use just role information or just context information, we do reasonably well… but if we use both, we do *much* better. While the question we’re answering here is quite specific, the advantage of our approach is that it can be applied to almost any social media context – any place online where users can both contribute content and interact with others. We could just as easily create a predictor for contributions to Yahoo! Answers, or even to Wikipedia (if we had the relevant data). This is definitely food for thought / opportunity for future work

Our (Vladimir D. Barash, Marc Smith, Lise Getoor, Howard T. Welser ) poster paper, Distinguishing Knowledge vs Social Capital in Social Media with Roles and Context  has been accepted and published at the 2009 ICWSM (International Conference on Weblogs and Social Media) conference which will be held in San Jose, California this May 17, 2009 – May 20, 2009.

Abstract
Social media communities (e.g. Wikipedia, Flickr, Live Q&A) give rise to distinct types of content, foremost among which are relational content (discussion, chat) and factual content (answering questions, problem-solving). Both users and researchers are increasingly interested in developing strategies that can rapidly distinguish these types of content. While many text-based and structural strategies are possible, we extend two bodies of research that show how social context, and the social roles of answerers can predict content type.  We test our framework on a dataset of manually labeled contributions to Microsoft’s Live Q&A and find that it reliably extracts factual and relational messages from the data.

Full Text: PDF: 2009 ICWSM Distingusihing knowledge versus social capital

Another conference focused on research on blogs and other forms of social media is “ICWSM” – the International Conference on Weblogs and Social Media.  I was able to attend the previous meeting of this conference last March in Seattle and give a talk about different classifications of social media and I am looking forward to attending this year’s meeting in San Jose.  Last year we had a poster paper in the conference about the ways some users in a blog system called Wallop were able to hold other users in the system.

Some Users Pack a Wallop: Measuring the Impact of Core Users on the Participation of Others in Online Social Systems
Thomas M. Lento, Eric Gleave, Marc A. Smith, Howard T. Welser

There was also a paper about the lessons learned from managing large corporate online community efforts.

Space Planning for Online Community
Danyel Fisher, Tammara Combs Turner, Marc A. Smith

This year, we have a poster in the conference that is focused on the ways network structures created when people reply to one another can be used to predict whether a message or thread is a question and answer exchange or a long discussion or debate.

Distinguishing Knowledge vs. Social Capital in Social Media with Roles and Context
Vladimir Barash, Marc Smith, Lise Getoor, Howard Welser

The conference attracts some great people and features the state of the art in research at the intersections of computer science, natural language processing, social network analysis, search engine/information retrieval design, information visualization, knowledge management and the social sciences.  That can be eclectic but this is the place for hearing about new work on Wikis, Blogs, Message Boards, and other social media systems like social networking services, micro-blogging systems, and mobile software.

The conference is held this year in May, from the 17th-20th, in San Jose, California.

Here are my pictures from last year’s ICWSM in 2008, held in Seattle, Washington.

There is also a nice picture from Joe McCarthy of Tom Lento and me in front of our poster at ICWSM 2008.

by gumption

Talk reviews sociological concepts of social media and visualizations of computer-mediated collective behavior.

Slides: