Searching Twitter: Separating the Tweet from the Chaff

1. Searching Twitter: Separating the Tweet from the Chaff Jonathan Hurlock & Max L. Wilson

2. You sure can!

3. llow I fo ? do ests H ow ter y In m http://www.ﬂickr.com/photos/stevegarﬁeld/5397972626/

4. http://www.ﬂickr.com/photos/apelad/3684843147/

5. Yet more Data Meta Data, Proﬁle Data, Linked Data

6. Any of it Useful? Who cares how much data there is! “I think the challenge not only for twitter, but for the technology industry at large. Is building more relevant ﬁlters, in real time. Like being able to surface valuable information immediately. No matter who it is, whoʼs listening or whoʼs broadcasting, is a really really hard problem, and it makes twitter alot more meaningful[... ]Weʼve gotten really really good at being able to put content in, into media [...] getting it out in a relevant, valueable way, in real time is still very difﬁcult.” - Jack Dorsey (Creator of Twitter)

7. Why Twitter? Where is the value? $ ₧ ƒ ! ₥ ₧ ₤ ₣ ¢ ₠! ! ₣£ ₡ ₱£ ! ₧ ₡ ¢ ₤ ₠ ₱! ₥ ₣ $ ƒ

8. Lets go back... http://www.ﬂickr.com/photos/milesdeelite/5309712846/

9. Lets go back... Great Scott! http://www.ﬂickr.com/photos/milesdeelite/5309712846/

10. Asking Friends Hey, what are you doing? you & me

11. Social Search What is everyone else doing? you & me

12. friend friend friend Social Search What is everyone else doing? friend you & me

13. bob & lisa Existing Knowledge No need to reinvent the wheel you & me Meredith Ringel Morris, Jaime Teevan, and Katrina Panovich. 2010. What do people ask their social networks, and why?: a survey study of status message & behavior. In Proceedings of the 28th international conference on Human factors in computing systems (CHI '10). ACM, New York, NY, USA, 1739-1748.

14. lisa Existing Knowledge bob & me No need to reinvent the wheel you Meredith Ringel Morris, Jaime Teevan, and Katrina Panovich. 2010. What do people ask their social networks, and why?: a survey study of status message & behavior. In Proceedings of the 28th international conference on Human factors in computing systems (CHI '10). ACM, New York, NY, USA, 1739-1748.

15. Lets go back to the network Remember... you & me

16. friend friend friend and if we take a step back... Please mind the gap friend you me

17. We start to see interesting things...

18. Which have value!

19. Location, experiences, temporal data Yardi, Sarita and Boyd, Danah. ICWSM 2010. http://www.flickr.com/photos/24423474@N08/4999891492/ http://www.flickr.com/photos/mdid/4560003881/ Tweeting from the Town Square: Measuring Geographic http://www.flickr.com/photos/seanhobson/3256437306/ Local Networks http://www.flickr.com/photos/gcaw/5445225362/ http://en.wikipedia.org/wiki/File:Plane_crash_into_Hudson_River_(crop).jpg

20. Location, experiences, temporal data Political upheaval, emergency events .. so what are you tweeting now? Yardi, Sarita and Boyd, Danah. ICWSM 2010. http://www.flickr.com/photos/24423474@N08/4999891492/ http://www.flickr.com/photos/mdid/4560003881/ Tweeting from the Town Square: Measuring Geographic http://www.flickr.com/photos/seanhobson/3256437306/ Local Networks http://www.flickr.com/photos/gcaw/5445225362/ http://en.wikipedia.org/wiki/File:Plane_crash_into_Hudson_River_(crop).jpg

21. Twitter Search How do you ﬁnd useful information?

22. Displaying Results Realtime

23. Displaying Results RT Time, ReTweets, Location, Popularity? http://www.ﬂickr.com/photos/publicenergy/394124407/

24. Displaying Results RT Time, ReTweets, Location, Popularity? http://www.ﬂickr.com/photos/publicenergy/394124407/

25. Displaying Results Making sense of the data.

26. Displaying Results Making sense of the data. Michael S. Bernstein, Bongwon Suh, Lichan Hong, Jilin Chen, Sanjay Kairam, Ed H. Chi. Eddi: Interactive Topic-based Browsing of Social Status Streams. In Proc. of ACM User Interface Software and Technology (UIST) conference, Oct. 2010. New York, NY.

27. Displaying Results Making sense of the data. Diakopoulos, N.; Naaman, M.; Kivran-Swaine, F.; , "Diamonds in the rough: Social media visual analytics for journalistic inquiry," Visual Analytics Science and Technology (VAST), 2010 IEEE Symposium on , vol., no., pp.115-122, 25-26 Oct. 2010

28. Interestingness Not necessarily useful! Naveed, Nasir and Gottron, Thomas and Kunegis, Jérôme and Alhadi, Arifah Che (2011) Bad News Travel Fast: A Content-based Analysis of Interestingness on Twitter. pp. 1-7. In: Proceedings of the ACM WebSci'11, June 14-17 2011, Koblenz, Germany. http://www.ﬂickr.com/photos/wwarby/2460655511/

29. How we are different? What makes us unique?

30. Finding Usefulness! What constitutes a useful Tweet? fuln ess use http://www.ﬂickr.com/photos/edduddiee/4346349664/

31. The Method How did we go about this?

32. Teevan, J., Ramage, D., & Morris, M. R. (2011). #TwitterSearch: a comparison of microblog search and web search. WSDM '11: Proceedings of the fourth ACM international conference on Web search and data mining (pp. 35-44). New York, NY, USA: ACM. Information Seeking 3 Information Seeking Tasks http://www.bbc.co.uk/proms/2010/share/badgewidget.shtml http://www.flickr.com/photos/ivyfield/4731067396/ http://www.flickr.com/photos/anniemole/241655156/

33. 20 Participants They were really nice people!

34. Search Interface A simple, easy to understand interface

35. It’s useful because... Think aloud + Interviews To help us provide more insight I didn’t because...

36. ∑ Analysis Lots and lots of it! K

37. Grounded Theory Inductive Coding = Lots of Post-its! Glaser, B. G., & Strauss, A. L. (2009). The Discovery of Grounded Theory: strategies for qualitative research. Piscataway, New Jersey, USA: Transaction Publishers.

38. Kappa Analysis Cohen... Fleiss.... Landis, R. J., & Koch, G. G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics , 33 (1), 159-174.

39. Extended Kappa Analysis Multi Coded Kappa 0.73 (Substantial Agreement) Between Evaluators & 0.62 (Substantial Agreement) with Independent Untrained Coder Harris, J. K., & Burke, R. C. (2005). Do you see what I see? An application of inter-coder reliability in qualitative analysis. American Public Health Association 133rd Annual Meeting & Exposition. Washington, DC, USA: American Public Health Association.

40. What did we ﬁnd? Useful & Not-Useful

41. Useful In Tweet Content Experience Someone reporting a personal experience, but not necessarily suggestion / direction. Direct Someone making a direct recommendation, but not necessarily relaying a personal experience. Recommendation Social Knowledge Containing information that is spreading socially, or becoming general knowledge. Specific Information Where facts are listed directly in tweets e.g. prices, times etc. Reflection on Tweet Entertaining The reader finds them amusing. Shared Sentiment The reader agrees with the author of the tweet. Relevant Time The time is current Location The location is relevant to the query.

42. Useful (cont.) Trust Trusted Author The twitter account has a reputation / following Trusted Avatar The visual appearance cultivates trust. Trusted Link A link to a trustworthy recognisable domain. Links Actionable Link The user can perform a transaction by using the link (heavily dependent on trust) Media Link The link is to rich multimedia content. Useful Link The link provides valuable information content, e.g. authoritative information, educated reviews Meta Tweet ReTweeted Lots Its information that others have passed on lots Conversation Its part of a series of tweets, and they all need to be useful

43. Not Useful Tweet Content No Information Absence of anything, event, factual points Introspective Personal content and personal thoughts for no social beneﬁt Off Topic Result not related to the query give / TF-IDF irrelevant Too Technical The content requires speciﬁc domain knowledge the resader doesn’t possess Poorly Constructed Tweets that may have grammatical / spelling errors, or malformed URLs. Bad Tweets SPAM Irrelevant or inappropriate messages Wrong Language Messages sent in a foreign language of that to the reader Dead Link A URL which does not work i.e. a 404 Not Relevant Time Out of date content Location Wrong geographic location

44. Not Useful (cont.) Trust Un-truested Author An author the reader feels at un-eased by or suspicious of. Un-trusted Link A link the reader feels is suspicious Subjective A tweet that is perspective centric, meaning the author is providing their view or projecting an Perspective Oriented attitude on a subject matter or to a subject / reader. Disagree with Tweet A conﬂict of aggreement between the reader and the author Not Funny A tweet that is aimed to be humorous, which the reader does not feel is humorous. Meta Tweet QnA Part of a conversation, reader desires the whole convo. not just the question or the answer. Repeated Content the reader has seen before.

45. Insights Interesting ﬁnds http://www.ﬂickr.com/photos/foxmulderven/3063598624

46. The Possible Impact Where could we see the impact of this work?

47. Search System A work in progress

48. Conclusions So just remember.

49. Thank you for Listening Jonathan Hurlock @jonhurlock Max L. Wilson @gingdottwit Like the talk? Then please tweet it, by quickly visiting: http://moourl.com/LikedTheTalk

Searching Twitter: Separating the Tweet from the Chaff

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