Twitter Research. Influence and the Social Graph

Transcription

1 Twitter Research Influence and the Social Graph

2 Outline Reading A Research Paper Four Research Papers Cha, Haddadi, Benevenutu & Gummadi - Measuring User Influence in Twitter: The Million Follower Fallacy Kwak, Chun & Noon - Fragile Online Relationship: A First Look at Unfollow Dynamics in Twitter Xu, Huang, Kwak & Contractor - Structures of Broken Ties: Exploring Unfollow Behavior on Twitter Sharma, Ghosh, Benevenuto, Ganguly & Gummadi - Inferring Whois-Who in the Twitter Social Network

3 Reading a Research Paper Motivation What is the research question and why are they asking it? Methods How did they collect the data? How did they analyze the data? How did they present the data? Results What did they find?

4 Measuring User Influence in Twitter: The Million Follower Fallacy

5 Cha et al. - Motivation In this paper, we present an empirical analysis of influence patterns in a popular social medium. Using a large amount of data gathered from Twitter, we compare three different measures of influence: indegree, retweets, and mentions. Are there other possible measures of 'influence' in Twitter?

6 Cha et al. - Methods Collection The Twitter dataset used in this paper consists of 2 billion follow links among 54 million users who produced a total of 1.7 billion tweets. They had access to the Twitter firehose

7 Cha et al. - Methods Analysis What was measured? Indegree influence - the number of followers of a user. Retweet influence - the number of retweets containing a given username. Mention influence - the number containing a given username. {did they control for retweet?}

8 Cha et al. - Methods Analysis Interesting tid-bit in the analysis: The network of Twitter users comprises a single disproportionately large connected component (containing 94.8% of users), singletons (5%), and smaller components (0.2%). The largest component contains 99% of all links and tweets. Ignored users with fewer than 10 tweets during their lifetime. Spearman s Rank correlation

9 Cha et al. - Results Most Influential: The most followed users: news sources (CNN, New York Times), politicians (Barack Obama), athletes (Shaquille O Neal), celebrities (Ashton Kutcher, Britney Spears) The most retweeted users: content aggregation services (Mashable, TwitterTips, TweetMeme), businessmen (Guy Kawasaki), news sites (The New York Times, The Onion) The most mentioned users were celebrities

10 Cha et al. - Results In general, users who get mentioned often also get retweeted often, and vice versa. {control for retweet?} When we tried normalizing the data, we identified local opinion leaders as the most influential. However, normalization failed to rank users with the highest sheer number of retweets as influential. Therefore, in this paper, we use the sheer number of retweets and mentions without normalizing these values by the total tweets of a user.

11 Cha et al. - Results Across Topics Most influential users are able to transcend topics they are influential over more than one topic Influence Over Time Top 10 most influential have high variation in the metrics

12 Fragile Online Relationship: A First Look at Unfollow Dynamics in Twitter

13 Kwak et al. - Motivation The two research questions explored here are: What are the characteristics of the unfollow behavior? Why do people unfollow others? Why bother study 'unfollowing'?

14 Kwak et al. - Methods Collection To address the first research question, we collected daily snapshots of the follow relationships of 1.2 million Koreanspeaking users over the course of 51 days as well as their tweets. For the second research question, we conducted interviews with 22 users to determine their motivations behind the unfollow behavior. Two specific collection periods needed Why?

15 Kwak et al. - Methods Analysis What were the techniques?

16 Kwak et al. - Results High reciprocity among Korean users G(I) 56-58%, G(II) 61-62% The average of unfollows per person is 15.4 in in G(I) and 16.1 in G(II). We conclude that unfollow is quite pervasive in Twitter. Follow is passive, low number of interaction Broken relations 4.1, unbroken 5.8 (average tweets)

17 Kwak et al. - Results 22 Interviews (11 male, 11 female) Recalled 3.32 cases of unfollowing Some Top Reasons Bursty Uninteresting Topics Mundane details of daily life Politics

18 Inferring Who-is-Who in the Twitter Social Network

19 Sharma et al. - Motivation present the design and evaluation of a novel who-iswho inference system for users on the popular Twitter microblogging site. Existing approaches to infer topics related to a user rely either on the profile information provided by the user herself or on analyzing the tweeting activity of the user We exploit the Lists feature on Twitter, which allows users to group together Twitter accounts posting on a topic of interest to them

20 Sharma et al. - Methods Dataset contains 54 million users who had 1.9 billion follow links among themselves and posted 1.7 billion tweets (as of August 2009) Nov 2011, re-crawled 54 million profiles for users Lists 1.3 million users listed at least 10 times (in other users lists) List titles as (1) separate words, (2) case folding, stemming, stop word removal, (3) part of speech tagger (POS), (4) edit distance (5) unigram and bigram Users are labeled with the terms from the Lists they are in

21 Sharma et al. - Results Evaluate against known accounts Well known users News media accounts US Senators Evaluate with human feedback