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 Copyright 2009 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute www.deri.ie Tadvise: A Twitter Assistant.

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Presentation on theme: " Copyright 2009 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute www.deri.ie Tadvise: A Twitter Assistant."— Presentation transcript:

1  Copyright 2009 Digital Enterprise Research Institute. All rights reserved. Digital Enterprise Research Institute Tadvise: A Twitter Assistant Based on Twitter Lists Peyman October 6, rd International Conference on Social Informatics (SocInfo'11), Singapore Management University, Singapore

2 Digital Enterprise Research Institute Who am I?

3 Digital Enterprise Research Institute My Talk

4 Digital Enterprise Research Institute Twitter Twitter is a micro-blogging service is a blogging service is a Web 2.0 service is Twitter Launched in Mid characters  To be SMSed >190 million users >750 tweets per second  Since I started this talk, perhaps 200k-500k new tweets posted

5 Digital Enterprise Research Institute What Is The Problem?

6 Digital Enterprise Research Institute Filtering

7 Digital Enterprise Research Institute Any Hubs? Among my followers, who may be a potential hub for a particular tweet? There exist communities in Twitter.

8 Digital Enterprise Research Institute User Profiles We need user profiles  Composed of users’ expertise, interests, communities, etc. What can we use for user profiles?  Tweets?  Favourite tweets?  Retweets?  ….  Twitter Lists

9 Digital Enterprise Research Institute People-Tagging

10 Digital Enterprise Research Institute Tadvise Among my followers, who may be a potential hub for a particular tweet? We developed Tadvise based on Twitter Lists  Tadvise Crawler –Crawls social network and Twitter lists  Tadvise User Profile Builder –Build user profiles for users including a ranked list of tags  Tadvise Advice Engine –Gets a tweet and a seed as inputs and recommend appropriate well- connected topic-sensitive hubs –It also shows if majority of followers are interested in a tweet (i.e., were tagged with a specific topic)

11 Digital Enterprise Research Institute Tadvise Crawler Crawler will be notified as soon as a user Twitter Account We use white-listed account Crawls the network of followers at distance of one and two of a seed (i.e., breath-first mechanism) Crawling Twitter Lists  Each API call returns 20 Twitter lists  We put a limit of 300 Twitter Lists

12 Digital Enterprise Research Institute Tadvise User Profile Builder Gets inputs from Tadvise Crawler Pre-Processing (e.g., stemming) Build Weighted User Profiles for each user  Rank tags –Rank users – in-degree on the number of followers  For the same tags, we sum up the weights Aggregate user profiles for all followers of a seed as well as followers at distance of two of a seed (next slide is an example)  Detecting majority and minority by applying k-means clustering When job done, send a direct message to user

13 Digital Enterprise Research Institute Using NLP techniques to extract seveal (38 followers) (before stemming) friend MMiiina(11)kelvinq(11)phauly(5)vietansegan(3) bkeegan(9) akhil_vinod(5) research epeyman(5)spartakan(6)phauly(10)winteram(6)ido_guy(5)ReaderMeter(4) social-media spartakan(8)kelvinq(17)mariusjoh(7)StuartWShulman(6) CloBonneau(8) science sugarpunk(8)SonicNU(1)NU_McCormick(8) ReaderMeter(13) academia phauly(5)StuartWShulman(6) NU_McCormick(8) socialmedia kelvinq(8)winteram(10)lizcampe(6) academics-researchers MMiiina(6)winteram(6)SonicNU(6) network-analysis MMiiina(8)bkeegan(8)SonicNU(15) social MMiiina(7)phauly(9)ReaderMeter(4) tech winteram(5)NU_McCormick(5) ReaderMeter(5) International phauly(7)sugarpunk(8) phd-research CarlosPC_Mx(6) texifter(6) singapore kelvinq(29)sugarpunk(15) edtech mariusjoh(7)StuartWShulman(4) asia kelvinq(8)sugarpunk(7) knowledge- management spartakan(8)KDialogues(14) technology spartakan(6)NU_McCormick(8) newmedia-academics StuartWShulman(8) CloBonneau(8) geeks spartakan(7)kelvinq(8)

14 Digital Enterprise Research Institute Tadvise UI

15 Digital Enterprise Research Institute Advice Engine (extracting key topics) Hashtags (e.g., #drupal)  Using tagdef.com, we analyse acronyms (#semweb, #SocInfo2011) Using NLP techniques to extract several tags Fetch URLs  Go to delicious.com and fetch tags associated with URLs Enriching tag sets using Google Sets or WordNet  Google Sets performed better (went offline on 5.Sep.2011)

16 Digital Enterprise Research Institute Advice Engine (majority vs. minority) Using followers‘ profiles, look how many people were tagged with any extracted tag from the tweet.

17 Digital Enterprise Research Institute Advice Engine (adding hubs) By looking at distance of two, who may propagate a tweet further to more relevant people.  Add them to a tweet (attract their attention - i.e., they might retweet)

18 Digital Enterprise Research Institute Text-based Explanation

19 Digital Enterprise Research Institute Radial Explanation

20 Digital Enterprise Research Institute Evaluation (hypotheses) Hypothesis 1: Twitter lists assist Twitter users to know each other better Hypothesis 2: Users find it difficult to keep track of their followers. Tadvise helps users to know their followers (as a whole) better by identifying their communities, interests, expertise, etc. Hypothesis 3: Tadvise helps users to propagate their (a) community- related and (b) non-community-related tweets more effectively by proposing well-connected followers for a particular topic We used personalized survey  Questionnaire for User Interaction Satisfaction (QUIS)  Was designed in a game-like fashion

21 Digital Enterprise Research Institute Evaluation (design) 1 st step: General Questions  Categorising Twitter lists 2 nd step: Usefulness of Twitter Lists/People-Tags  Showing random followers to the subject and asking her if Twitter lists help to know this follower better 3 rd step: Knowledge of Followers  Showing a random Twitter list to the subject and asking her to select who may be assigned to that list 4 th step: Usefulness of Recommendations  Showing random topics to the subject and asking her to select two followers who may have the most followers related to that topic 5 th step: General Questions  Usefulness of such systems for finding hubs

22 Digital Enterprise Research Institute Evaluation (participants) Survey was online for four weeks 112 Twitter users  101 eligible users 76 participated  66 completed 47% of participants, who completed the survey (i.e., 31 participants) had 100 or more followers  among them 12 participants had more than 500 followers  4 participants had 1000 or more followers A wide range of users  students, researchers, marketers, technicians, professors, community fundraisers, etc.

23 Digital Enterprise Research Institute Evaluation (key messages) 64.7% of participants mentioned that they tweet at least once per week  3% mentioned that they do at least once per hour Twitter lists categories  Affiliation: 24.3%, Interest and Hobby: 15.9%, Technology: 14.6%, Skill and Expertise: 13.8%, Working Group: 9.2%, Location: 8.4%, Characteristic: 6.3%, Project: 3.8%, Role: 1.7%, Name: 1.3%, and Sport: 0.8% 79.1% of participants who were assigned to one or more Twitter lists mentioned that Twitter lists represent them correctly, whereas only 1.6% mentioned it is incorrect (i.e., validity period of lists?) 78.1% of participants were positive about being recommended topic- sensitive potential hubs, whereas 7.8% found it useless Our subjects found Tadvise recommendations for (mainly) community-related tweets useful and convincing

24 Digital Enterprise Research Institute Evaluation Results Hypothesis 1 (58.1% +, 18.6% -)Hypothesis 2 (57.4% +, 17.3% -) Hypothesis 3(a) (72% +, 13.7% -)Hypothesis 3(b) (49.3% +, 18.2% -)

25 Digital Enterprise Research Institute Demo Wanna See a Live Demo?

26 Digital Enterprise Research Institute Conclusions Keeping track of followers become more and more difficult  Some users pay attention to their followers Tadvise builds user profiles for Twitter users and later leverages such profiles for tweet propagation purposes Our evaluation suggests that participants were mainly interested in being recommended with topic-sensitive community-related hubs (i.e., 72% +) Future Work  Building more comprehensive user profiles using other sources like tweets and retweets  Adding refractory period for users, so that not all (attention) requests be targeted in a short space of time to specific hubs  Enabling users to manipulate their profiles

27 Digital Enterprise Research Institute

28 Digital Enterprise Research Institute Credits

29 Digital Enterprise Research Institute Try Tadvise Using NLP techniques to extract seveal

30 Digital Enterprise Research Institute Keywords Twitter Tadvise Twitter assistant Twitter-lists communities community-hubs user-profiles community-tweets tweet-propagation Using NLP techniques to extract seveal tagsdsqdasdas


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