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

Cody’s piece is interesting and well-reasoned. The basic argument he makes is that Twitter, much like Myspace, became popular before it new what it was, and is now suffering an identity crisis that will force it to fracture / fade into obscurity and be replaced by more focused applications. The comparison of Twitter to Myspace, however, falls short: in one sentence, I would claim that Twitter will not go the way of Myspace, because Myspace is more of an environment, whereas Twitter is a platform.

It’s true, both Twitter and Myspace suffer from lack of clear vision and perhaps an overabundance of uses. It’s a citizen journalism service, a way to catch up / chat with your friends, a procrastination device, a way for fans to follow celebrities, etc. You can use Twitter to write novels and play chess. This was the problem with Myspace: it offered users unlimited means of self-expression without a single overarching paradigm. When users were bored with expressing themselves (as all users inevitably are), there was nothing solid to keep them on the site, and so they drifted. But Twitter does have a single overarching paradigm: the tweet-stream.

Twitter’s greatest use is as a low-level service to provide individuals with a socially filtered, digestible, flexible stream of information. E-mail doesn’t provide digestible streams: the limit to the length of an e-mail is MUCH bigger than 140 characters. RSS is not socially filtered. Social bookmarking is both digestible and socially filtered, but less flexible – it doesn’t allow users to engage in dialogue and commentary as part of the bookmarking stream. Facebook has profiles and pictures and events which make the stream (news feed) much less digestible. 

Does this mean that Twitter is perfect? Not at all. I think Twitter is going through an identity crisis (though that crisis is going to happen more on the surface and the periphery, and not detract from the utility of the core service). In my opinion, the way out of that crisis lies in going back to the core, to the socially filtered, digestible flexible information stream. There have been a number of apps built on top of Twitter, but, to my knowledge, these apps have yet to fully harness the stream. First step is search, which is happening and is a good thing. Second step should be better filtering – I want real-time manipulation of my tweet-stream to filter out posts by person X. Third step should be extracting social interactions from pure information streams – I want to grab all the @posts I’ve had with X, and their responses, and the responses of all of my other friends who have seen these @posts and commented on them. I want an interactive social graph plugin that I can drag / click on to expose tweets between / by different subsets of my followers, or followees, or both. These features would not only help me manage incoming tweets, but also help me organize my stream, and give me more control over it. 

So these are some thoughts about Twitter, why it’s not going the way of MySpace, and how it could be better. I hope that the features I listed are either a) already available and I don’t know about them, or b) will be developed soon. Twitter is a great platform and has been incredibly useful for my own social media management. I hope to see it grow and improve in the coming months!

Peter Pirolli, research at PARC, presented the keynote about the challenges and opportunities for the use of social media to address social problems.

There is growing interest in this space, for example NSF funding was significantly increased this year. For example, there is the new NSF Social-Computational Systems (SoCS) program: http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=503406&org=NSF&sel_org=NSF&from=fund

President Obama’s recent speech to the National Academy of Science (http://www.nasonline.org/site/PageServer) sets some of the context for this group’s vision, he speaks about crowd sourcing and its impact on science. (See minute 28:30) video is on the web site of the National Academy of Science http://edg1.vcall.com/video/nas/launch.asp

“I have charged the White House Office of Science and Technology Policy with leading a new effort to ensure that federal policies are based on the best and most unbiased scientific information. I want to be sure that facts are driving scientific decisions – and not the other way around.

As part of this effort, we’ve already launched a website that allows individuals to not only make recommendations to achieve this goal, but to collaborate on those recommendations; it is a small step, but one that is creating a more transparent, participatory and democratic government.”

Elsewhere in the speech there is a refernce to the role of (appropriately) digitizing medical records which I think includes the idea that people will increasingly gather online to work towards better personal health:

“The Recovery Act will support the long overdue step of computerizing America’s medical records, to reduce the duplication, waste, and errors that cost billions of dollars and thousands of lives.

But it’s important to note: these records also hold the potential of offering patients the chance to be more active participants in prevention and treatment. We must maintain patient control over these records and respect their privacy. At the same time, however, we have the opportunity to offer billions and billions of anonymous data points to medical researchers who may find in this information evidence that can help us better understand disease.”

There seems to be a role for the kinds of online communities and social media that people turn to when facing physical or medical challenges.

National Initiative for Social Participation at University of Maryland
http://iparticipate.wikispaces.com/Motivating+Scenario
http://chronicle.com/daily/2009/05/17280n.htm
http://www.flickr.com/photos/marc_smith/sets/72157617551332795/

[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

————————————————————————————————————————————————-

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