What am I not Seeing? An Interactive Approach to Social Content Discovery in Microblogs
Pages 279 - 294
Abstract
In this paper, we focus on the informational and user experience benefits of user-driven topic exploration in microblog communities, such as Twitter, in an inspectable, controllable and personalized manner. To this end, we introduce “HopTopics” – a novel interactive tool for exploring content that is popular just beyond a user’s typical information horizon in a microblog, as defined by the network of individuals that they are connected to. We present results of a user study (N=122) to evaluate HopTopics with varying complexity against a typical microblog feed in both personalized and non-personalized conditions. Results show that the HopTopics system, leveraging content from both the direct and extended network of a user, succeeds in giving users a better sense of control and transparency. Moreover, participants had a poor mental model for the degree of novel content discovered when presented with non-personalized data in the Inspectable interface.
References
[1]
Amar RA and Stasko JT Knowledge precepts for design and evaluation of information visualization IEEE Trans. Vis. Comput. Graph. 2005 11 432-442
[2]
André, P., Schraefel, M.C., Teevan, J., Dumais, S.T.: Discovery is never by chance: Designing for (un)serendipity. In: Creativity and Cognition (2009)
[3]
Bennett, S.W., Scott, A.C.: The rule-based expert systems: The MYCIN experiments of the stanford heuristic programming project, chapter 19 - specialized explanations for dosage selection, pp. 363–370. Addison-Wesley Publishing Company (1985)
[4]
Bernstein, M.S., Suh, B., Hong, L., Chen, J., Kairam, S., Chi, E.H.: Eddi: Interactive topic-based browsing of social status streams. In: User Interface Software and Technology, UIST 2010, pp. 303–312 (2010)
[5]
Broersma M and Graham T Twitter as a news source J. Pract. 2013 7 4 446-464
[6]
Brusilovsky P, Schwarz E, and Weber G Frasson C, Gauthier G, and Lesgold A ELM-ART: an intelligent tutoring system on world wide web Intelligent Tutoring Systems 1996 Heidelberg Springer 261-269
[7]
Cerutti, F., Tintarev, N., Oren, N.: Formal arguments, preferences, and natural language interfaces to humans: an empirical evaluation. In: ECAI, pp. 207–212 (2014)
[8]
Dimitrova V Style-olm: Interactive open learner modelling Int. J. Artif. Intell. Educ. 2003 17 2 35-78
[9]
Garcia Esparza, S., O’Mahony, M.P., Smyth, B.: Catstream: Categorising tweets for user profiling and stream filtering. In: International Conference on Intelligent User Interfaces, IUI 2013, pp. 25–36 (2013)
[10]
Granovetter MS The strength of weak ties Am. J. Sociol. 1973 78 6 1360-1380
[11]
Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: ACM Conference on Computer Supported Cooperative Work, pp. 241–250 (2000)
[12]
Herlocker JL, Konstan JA, Terveen L, and Riedl JT Evaluating collaborative filtering recommender systems ACM Trans. Inf. Syst. 2004 22 1 5-53
[13]
Knijnenburg BP, Willemsen MC, Gantner Z, Soncu H, and Newell C Explaining the user experience of recommender systems User Model. User Adap. Inter. 2012 22 4–5 441-504
[14]
Kulesza, T., Burnett, M., Wong, W.-K., Stumpf, S.: Principles of explanatory debugging to personalize interactive machine learning. In: IUI (2015)
[15]
Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: International Conference on World Wide Web, WWW 2010, pp. 591–600 (2010)
[16]
Marcus, A., Bernstein, M.S., Badar, O., Karger, D.R., Madden, S., Miller, R.C.: Twitinfo: Aggregating and visualizing microblogs for event exploration. In: Conference on Human Factors in Computing Systems, CHI 2011, pp. 227–236 (2011)
[17]
Nagulendra S and Vassileva J Antunes P, Gerosa MA, Sylvester A, Vassileva J, and Vreede G-J Providing awareness, understanding and control of personalized stream filtering in a P2P social network Collaboration and Technology 2013 Heidelberg Springer 61-76
[18]
Paramythis A, Weibelzahl S, and Masthoff J Layered evaluation of interactive adaptive systems: Framework and formative methods User Model. User Adap. Interact. 2010 20 2-12
[19]
Pariser E The filter bubble: What the Internet is hiding from you 2011 New York Penguin Books
[20]
Schaffer, J., Giridhar, P., Jones, D., Höllerer, T., Abdelzaher, T., O’Donovan, J.: Getting the message?: A study of explanation interfaces for microblog data analysis. In: Intelligent User Interfaces, IUI 2015, pp. 345–356 (2015)
[21]
Sharma, A., Cosley, D.: Do social explanations work? studying and modeling the effects of social explanations in recommender systems. In: World Wide Web (WWW) (2013)
[22]
Smyth B and McClave P Aha DW and Watson I Similarity vs. diversity Case-Based Reasoning Research and Development 2001 Heidelberg Springer 347-361
[23]
Teevan J, Morris MR, and Azenkot S Supporting interpersonal interaction during collaborative mobile search Computer 2014 47 3 54-57
[24]
Tintarev, N., Kang, B., ODonovan, J.: Inspection mechanisms for community-based content discovery in microblogs. In: IntRS15 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at ACM Recommender Systems (2015)
[25]
Tintarev Nava and Masthoff Judith Explaining Recommendations: Design and Evaluation Recommender Systems Handbook 2015 Boston, MA Springer US 353-382
[26]
Verbert, K., Parra, D., Brusilovsky, P., Duval, E.: Visualizing recommendations to support exploration, transparency and controllability. In: International Conference on Intelligent User Interfaces, IUI 2013, pp. 351–362 (2013)
[27]
Wang, B., Ester, M., Bu, J., Cai, D.: Who also likes it? generating the most persuasive social explanations in recommender systems. In: AAAI Conference on Artificial Intelligence (2014)
Recommendations
Personalized Microtopic Recommendation on Microblogs
Survey Paper, Regular Papers and Special Issue: Social Media ProcessingMicroblogging services such as Sina Weibo and Twitter allow users to create tags explicitly indicated by the # symbol. In Sina Weibo, these tags are called microtopics, and in Twitter, they are called hashtags. In Sina Weibo, each microtopic has a ...
Microblogs Data Management Systems: Querying, Analysis, and Visualization
SIGMOD '16: Proceedings of the 2016 International Conference on Management of DataMicroblogs data, e.g., tweets, reviews, news comments, and social media comments, has gained considerable attention in recent years due to its popularity and rich contents. Nowadays, microblogs applications span a wide spectrum of interests, including ...
Identifying fact-checkable microblogs during disasters: a classification-ranking approach
ICDCN '19: Proceedings of the 20th International Conference on Distributed Computing and NetworkingMicroblogging sites are increasingly playing an important role in real-time disaster management. However, rumors and fake news often spread on such platforms, which if not detected, can derail the rescue operations. Therefore, it becomes imperative to ...
Comments
Information & Contributors
Information
Published In
Nov 2016
527 pages
© Springer International Publishing AG 2016.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 11 November 2016
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 03 Feb 2025