To Thread or Not to Thread: The Impact of Conversation Threading on Online Discussion

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To Thread or Not to Thread The Impact of Conversation Threading

on Online Discussion

Pablo Aragón*‡, Vicenç Gómez*, Andreas Kaltenbrunner‡

* Universitat Pompeu Fabra‡ Eurecat, Technology Centre of Catalonia

The 11th International AAAI Conference on Web and Social Media (ICWSM-17) — Montreal. May 16-18, 2017

Online communities

● Members interact with each other primarily via the Internet

● They are commonly built by strangers

● Online discussion is an essential mechanism of both communication and collaboration

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ICWSM-17 — Montreal, Apr 16-18, 2017

Reciprocity in online communities

● Many members do reciprocate support (Wellman et al, 1999)

● A behavioral indicator for their emergence (Herring et al, 2004)

● A sign of an inward focus and vigorous debate (Fisher et al, 2006)

● An indicator of captivating/engaging communication (Rafaeli et al, 1997)

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Hierarchical

Messages are

arranged close to

their replies in a

tree-like structure

making reciprocal

interactions

explicit

Linear

Messages are

presented in order

(chronological)

regardless of

reply

relationships

Conversation threading (or not)

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Does conversation threading matter? How can we model its impact on online discussion?

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Related work

Conversation threading

● provides better local context (Venolia et al, 2003)

● mitigates the ‘co-text’ loss problem (Fuks et al, 2006)

users are better distinguish the earlier message to which a particular message replies to

● favors knowledge construction (McVerry, 2007)

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Related work

Generative models to characterize the structural properties of online discussion threads

● Usenet, Yahoo Groups, Twitter (Kumar et al, 2010)

● Digg, Reddit, Epinions (Wang et al, 2012)

● Slashdot, Barrapunto, Menéame, Wikipedia (Gómez et al, 2013)

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Research gap

● Previous studies of conversation threading

○ were based on small groups of recruited participants

○ never included a modeling approach

● State-of-the-art generative models of online discussion

○ failed to capture the deep structures of discussion threads

○ never incorporated reciprocity as a feature

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Research questions

RQ1 How does conversation threading affect reciprocity withinthe discussion of an online community?

RQ2 Is reciprocity a key behavioral feature when modeling thestructure and growth of discussion threads?

RQ3 How does conversation threading affect the behavioralfeatures when modeling the structure and growth of discussion threads?

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Menéame: a unique case-study

● The most popular Spanish social news site

● Online community of thousands of users who daily debate hundreds of stories

● Conversation threading was adopted in Jan 2015

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Until Jan 2015 Since Jan 2015

Dataset description

72,005 stories and 5,385,324 comments (2011-2015)

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Measuring conversation threading effects

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Reciprocity in the network of users of each thread

Standard Reciprocity

Corrected reciprocity (Garlaschelli et al, 2004)

Weighted reciprocity (Squartini et al, 2013)

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Regression discontinuity design

Statistical analysis of platform effects (Malik et al, 2016):

“The design and technical features of a given platform which constrain, distort, and shape user behavior on that platform”

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Results

Reciprocity increases with conversation threading (Jan 2015)

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Modeling reciprocal online discussions

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Thread visualization tool (Aragón et al, 2016)

Exploration of discussion threads

Thread in 2013 Thread in 2015 (linear view) (hierarchical view) 17


Generative models of discussion threadsExtension of Goméz et al (2013)

Popularity (α) Comments with many replies aremore appealing (pref. attachment)

Root bias (β) Distinction between the post (the initial node) and the comments

Novelty (τ) Old comments gradually become lessattractive than new ones

Reciprocity (κ) Users tend to comment replies to their previous comments

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Results: Performance

The generative model including reciprocity better reproduces the typical deep structures of online discussion threads

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Results: Behavioural features

The behavioural features undergo an notable increase when conversation threading is released (Jan 2015)

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Conclusion, limitations and future work

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Conclusion

RQ1 How does conversation threading affect reciprocity within the discussion of an online community?

Although reciprocity naturally increases over time, there is an additional boost when conversation threading is adopted

Positive indicator for online communities

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Conclusion

RQ2 Is reciprocity a key behavioral feature when modeling the structure and growth of discussion threads?

The new generative model, with reciprocity, reproduces more accurately the depth of discussion threads

Better characterization of online discussion dynamics

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Conclusion

RQ3 How does conversation threading affect the behavioral features when modeling the structure and growth of discussion threads?

More reciprocal behavior, popular comments are even moreattractive, and the decay of novelty slows down

Menéame does not apply comment folding

The first comments receive larger attention24


Our methodology is language-independent:

● can be easily applied to any other platform,

● but ignores content-based features

Exploration of how conversation threading might affect:

● the hierarchical distribution of topics (Weninger et al, 2013)

● linguistic indications of reciprocity (Althoff et al, 2014)

Limitations and future work

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Althoff, T., Danescu-Niculescu-Mizil, C., & Jurafsky, D. (2014). How to Ask for a Favor: A Case Study on the Success of Altruistic Requests. In Eighth International AAAI Conference on Weblogs and Social Media.

Aragón, P., Gómez, V., & Kaltenbrunner, A. (2016). Visualization Tool for Collective Awareness in a Platform of Citizen Proposals. In Tenth International AAAI Conference on Web and Social Media.

Fisher, D., Smith, M., & Welser, H. T. (2006). You are who you talk to: Detecting roles in usenet newsgroups. In System Sciences, 2006. HICSS'06. Proceedings of the 39th Annual Hawaii International Conference on (Vol. 3, pp. 59b-59b). IEEE.

Fuks, H., Pimentel, M., & Pereira de Lucena, C. J. (2006). RU-Typing-2-Me? Evolving a chat tool to increase understanding in learning activities. International Journal of Computer-Supported Collaborative Learning, 1(1), 117-142.

Garlaschelli, D., & Loffredo, M. I. (2004). Patterns of link reciprocity in directed networks. Physical review letters, 93(26), 268701.

References

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Gómez, V., Kappen, H. J., Litvak, N., & Kaltenbrunner, A. (2013). A likelihood-based framework for the analysis of discussion threads. World Wide Web, 16(5-6), 645-675.

Herring, S. C., Barab, S., Kling, R., & Gray, J. (2004). An approach to researching online behavior. Designing for virtual communities in the service of learning, 338.

Kumar, R., Mahdian, M., & McGlohon, M. (2010). Dynamics of conversations. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 553-562). ACM.

Malik, M. M., & Pfeffer, J. (2016). Identifying Platform Effects in Social Media Data. In Tenth International AAAI Conference on Web and Social Media.

McVerry, J. G. (2007). Forums and functions of threaded discussions. New England Reading Association Journal, 43(1), 79.

Rafaeli, S., & Sudweeks, F. (1997). Networked interactivity. Journal of Computer‐Mediated Communication, 2(4).

References

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Squartini, T., Picciolo, F., Ruzzenenti, F., & Garlaschelli, D. (2013). Reciprocity of weighted networks. Scientific reports, 3, 2729.

Venolia, G. D., & Neustaedter, C. (2003). Understanding sequence and reply relationships within email conversations: a mixed-model visualization. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 361-368). ACM.

Wang, C., Ye, M., & Huberman, B. A. (2012). From user comments to on-line conversations. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 244-252). ACM.

Wellman, B., & Gulia, M. (1999). : Virtual communities as communities. Networks in the global village, 331-366.

Weninger, T., Zhu, X. A., & Han, J. (2013). An exploration of discussion threads in social news sites: A case study of the reddit community. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 579-583). ACM.

References

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Thank you@elaragon

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https://elaragon.wordpress.com/




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