Online discussion is essential for the communication and collaboration of online communities. The reciprocal exchange of messages between users that characterizes online discussion can be represented in many different ways. While some platforms display messages chronologically using a simple linear interface, others use a hierarchical (threaded) interface to represent more explicitly the structure of the discussion. Although the type of representation has been shown to affect communication, to the best of our knowledge, the impact of using either one or the other has not yet been investigated in a large and mature online community. In this work we analyze Menéame, a popular Spanish social news platform which recently transitioned from a linear to a hierarchical interface, becoming an ideal research opportunity for this purpose. Using interrupted time series analysis and regression discontinuity design, we observe an abrupt and significant increase in social reciprocity after the adoption of a threaded interface. We furthermore extend state-ofthe-art generative models of discussion threads by including reciprocity, a fundamental feature to explain better the structure of the discussions, both before and after the change in the interface.

1. 1
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

2. 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
2
ICWSM-17 — Montreal, Apr 16-18, 2017

3. 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)
3
ICWSM-17 — Montreal, Apr 16-18, 2017

4. 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)
4
ICWSM-17 — Montreal, Apr 16-18, 2017

5. Does conversation threading matter?
How can we model its impact on online discussion?
5
ICWSM-17 — Montreal, Apr 16-18, 2017

6. 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)
6
ICWSM-17 — Montreal, Apr 16-18, 2017

7. 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)
7
ICWSM-17 — Montreal, Apr 16-18, 2017

8. 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
8
ICWSM-17 — Montreal, Apr 16-18, 2017

9. Research questions
RQ1 How does conversation threading affect reciprocity within
the discussion of an online community?
RQ2 Is reciprocity a key behavioral feature when modeling the
structure and growth of discussion threads?
RQ3 How does conversation threading affect the behavioral
features when modeling the structure and growth of
discussion threads?
9
ICWSM-17 — Montreal, Apr 16-18, 2017

10. 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
10
ICWSM-17 — Montreal, Apr 16-18, 2017
Until Jan 2015 Since Jan 2015

11. Dataset description
72,005 stories and 5,385,324 comments (2011-2015)
11
ICWSM-17 — Montreal, Apr 16-18, 2017

12. Measuring conversation threading effects
12
ICWSM-17 — Montreal, Apr 16-18, 2017

13. Reciprocity in the network of users of each thread
Standard Reciprocity
Corrected reciprocity (Garlaschelli et al, 2004)
Weighted reciprocity (Squartini et al, 2013)
13
ICWSM-17 — Montreal, Apr 16-18, 2017

14. 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”
14
ICWSM-17 — Montreal, Apr 16-18, 2017

15. Results
Reciprocity increases with conversation threading (Jan 2015)
15
ICWSM-17 — Montreal, Apr 16-18, 2017

16. Modeling reciprocal online discussions
16
ICWSM-17 — Montreal, Apr 16-18, 2017

17. Thread visualization tool (Aragón et al, 2016)
Exploration of discussion threads
Thread in 2013 Thread in 2015
(linear view) (hierarchical view) 17
ICWSM-17 — Montreal, Apr 16-18, 2017

18. Generative models of discussion threads
Extension of Goméz et al (2013)
Popularity (α) Comments with many replies are
more appealing (pref. attachment)
Root bias (β) Distinction between the post
(the initial node) and the comments
Novelty (τ) Old comments gradually become less
attractive than new ones
Reciprocity (κ) Users tend to comment replies to
their previous comments
18
ICWSM-17 — Montreal, Apr 16-18, 2017

19. Results: Performance
The generative model including reciprocity better reproduces
the typical deep structures of online discussion threads
19
ICWSM-17 — Montreal, Apr 16-18, 2017

20. Results: Behavioural features
The behavioural features undergo an notable increase when
conversation threading is released (Jan 2015)
20
ICWSM-17 — Montreal, Apr 16-18, 2017

21. Conclusion, limitations and future work
21
ICWSM-17 — Montreal, Apr 16-18, 2017

22. 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
22
ICWSM-17 — Montreal, Apr 16-18, 2017

23. 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
23
ICWSM-17 — Montreal, Apr 16-18, 2017

24. 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 more
attractive, and the decay of novelty slows down
Menéame does not apply comment folding
The first comments receive larger attention
24
ICWSM-17 — Montreal, Apr 16-18, 2017

25. 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
25
ICWSM-17 — Montreal, Apr 16-18, 2017

26. 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
26
ICWSM-17 — Montreal, Apr 16-18, 2017

27. 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
27
ICWSM-17 — Montreal, Apr 16-18, 2017

28. 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
28
ICWSM-17 — Montreal, Apr 16-18, 2017

29. 29
Thank you
@elaragon
https://elaragon.wordpress.com
pablo.aragon@upf.edu