On Participation in Group Chats in Twitter

1. On Participation in Group Chats in Twitter Yeung Fu Sing

2. Agenda

3. Introduction • Humans are social animals • Interact with others • Watch movies with friends • Attend lectures • Chat with others

4. Twitter chats • Open to public • About a specific topic • e.gMaths (with hash tag #mathchat) • Designated time • Retweet, mention, reply during chats

5. Tweets Differential Equations are so annoying @user123 #mathchat • “Differential Equation are …” is the tweet content • @user123 is that the tweet issuer is replying a tweet by user123 or mentioning user123 • #mathchat means the user is participating in the chat mathchat

6. Will a new member of a chat participated in the chat again?

7. Methodology • Analyze twitter chats over two years • Consider chats when a new user join • Identify characteristic of a chat • By analyzing the tweets in the session • Model with logistic regression on whether the user will return

8. Previous researches • Mainly about offline groups • Factors include: • Individual characteristics • Group characteristics • Social inclusion • Use of languages • Geographical factors

9. 5-factor Model (5F Model) • Individual initiative • Group characteristics • Perceived receptivity • Linguistic affinity • Geographic proximity

10. Individual Initiative • How one act in a group • Active person may have higher chance to come back Measurements • Tweets contributes - Urls provided - No. of mentions - Retweets

11. Group Characteristics • How the other people act is the session • Characteristics of a group: • Amount of information • Conformance • Inter-member relations • Group maturity

12. Group Characteristics • Measures • No. of tweets - No. of urls • No. of retweets - No. of mentions • The age of a group

13. Perceived Receptivity • How a new user feel by the group • Is he accepted or ignored? • Measures • Whether the user is mentioned • Whether the user is retweeted

14. Linguistic Affinity • Language is important in social interactions • Linguistic similarity positively correlated to lasting relationship • Compare the similarity • Linguistic Inquiry Word Count (Software) • Each user represented by a vector • Pearson correlation to identify degree of similarity

15. Geographic Proximity • People tends to join groups close to him • Recent research oppose to this idea • Measures • Mean distance of user of chat and the new user

16. Analysis • Examined >100 chats • Use chats with over 10 sessions • 30 chats obtained • P-value to see whether is significance • Nagelkerke R2 Index to compare the models

18. Retweet, url provided has negative correlation • Retweeting a friend’s tweet, not participating in the chat • If user provide a url, he may be already familiar with the chat topic • Number of tweets is positively correlated

19. Group characteristics contribute less • Low pseudo-R value • Only group maturity is more significant • Too much information prevent user return • Group established for a long time also do so

20. Perceived Receptivity is significance • Good indicator in making prediction • High R value, accurate model • Reasonable result

21. Highest coefficient and Pseudo-R value • Very significance in user returning to a chat • In line with researches

22. Discussion • Social inclusion is important • Similar use of words is significant • Group characteristics is less significant • Geographic location is insignificant

23. User Survey • Complement the result from the model • 26 Questions • 60 responds

24. Findings of the survey • Value informational support • Sense of community is important • Diversity of background and geographical location is good • Pace and information is a disadvantage • Mature groups become close to new memeber