Statistical analysis of the social network and discussion threads in slashdot

By Vicenç Gómez, Andreas Kaltenbrunner, Vicente López. The Web Conference, 2008

328 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/4b4cd5f217cb3f2078e22b30860364a6d5c73604

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Citations in the corpus

Characterizing and curating conversation threads: expansion, focus, volume, re-entry (2013, background): “On (c), there is intriguing work [12, 15] on structural characterizations of discussions when viewed as trees (not an approach we have taken in this paper, and arguably less natural as a model for discussions on sites like Facebook that have post-and-commentinterfaces).”
Predicting the popularity of online content (2008, background): “Activity fluctuations, user commenting behavior prediction, the ensuing social network, and community moderation structure is the focus of studies on Slashdot [15, 12 , 17], a portal that is similar in spirit to Digg.”
Using a model of social dynamics to predict popularity of news (2010, background): “Although early and late popularity, which can be measured in terms of the number of views or votes an item generates, are somewhat correlated [7, 22], we know little about what drives success.”
Chameleons in Imagined Conversations: A New Approach to Understanding Coordination of Linguistic Style in Dialogs (2011, methodology): “Also, our results on contention could be used to further automatic controversy detection (Mishne and Glance, 2006; Gómez et al., 2008).”
4chan and /b/: An Analysis of Anonymity and Ephemerality in a Large Online Community (2011, background): “The closest comparison available in the literature is that anonymous commenting makes up 18.6% of Slashdot comments (Gómez, Kaltenbrunner, and López 2008).”
The slashdot zoo: mining a social network with negative edges (2009, unknown): “The Slashdot discussion threads are studied in [6].”
How Community Feedback Shapes User Behavior (2014, background): “…sites and blogs, has also been used to study other social phenomena such as controversy (Chen and Berger 2013), political polarization (Park et al. 2011; Balasubramanyan et al. 2012), and community formation (Gómez, Kaltenbrunner, and López 2008; Gonzalez-Bailon, Kaltenbrunner, and Banchs 2010).”
Comparison of online social relations in volume vs interaction: a case study of cyworld (2008, methodology): “We substitute aij in (4) with wij and recalculate the reciprocity coefficient to obtain 0:7650: Compared to 0:5165 of the World Wide Web network [16], 0:231 of Email Networks [16], 0:28 of Slashdot [ 19 ], 0:58 of Twitter [24], and 0:32 of wikipedia [45], the quantitative link reciprocity of the Cyw”