Characterizing and curating conversation threads: expansion, focus, volume, re-entry

By Lars Backstrom, Jon M. Kleinberg, Lillian Lee, Cristian Danescu-Niculescu-Mizil. 2013

120 citations recorded by the source corpus.

Discussion threads form a central part of the experience on many Web sites, including social networking sites such as Facebook and Google Plus and knowledge creation sites such as Wikipedia. To help users manage the challenge of allocating their attention among the discussions that are relevant to them, there has been a growing need for the algorithmic curation of on-line conversations --- the development of automated methods to select a subset of discussions to present to a user. Here we consider two key sub-problems inherent in conversational curation: length prediction --- predicting the number of comments a discussion thread will receive --- and the novel task of re-entry prediction --- predicting whether a user who has participated in a thread will later contribute another comment to it. The first of these sub-problems arises in estimating how interesting a thread is, in the sense of generating a lot of conversation; the second can help determine whether users should be kept notified of the progress of a thread to which they have already contributed. We develop and evaluate a range of approaches for these tasks, based on an analysis of the network structure and arrival pattern among the participants, as well as a novel dichotomy in the structure of long threads. We find that for both tasks, learning-based approaches using these sources of information.

Subfields: Artificial Intelligence, Communication

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Citations in the corpus

Smart Reply: Automated Response Suggestion for Email (2016, background): “…distribution can be factorized as: P (r1, ..., rm|o1, ..., on) = m∏ i=1 P (ri|o1, ..., on, r1, ..., ri−1) First, the sequence of original message tokens, including a special end-of-message token on, are read in, such that the LSTM’s hidden state encodes a vector representation of the whole message.”
Debiasing social wisdom (2013, background): “Similarly, on online sites that support discussion threads [1], a user’s participation history could be used to obtain an approximate estimate of her α values.”
No country for old members: user lifecycle and linguistic change in online communities (2013, background): “Furthermore, since both communities were active for over a decade, we can observe multiple generations of users simultaneously, and therefore discard external effects.”
Conversations Gone Awry: Detecting Early Signs of Conversational Failure (2018, methodology): “In this sense, we build on prior work in modeling conversation trajectory, which has largely considered structural aspects of the conversation (Kumar et al., 2010; Backstrom et al., 2013).”
Something’s Brewing! Early Prediction of Controversy-causing Posts from Discussion Features (2019, background): “, 2018b,a), or subsequent comment volume/popularity/structure (Szabo and Huberman, 2010; Kim et al., 2011; Tatar et al., 2011; Backstrom et al., 2013; He et al., 2014; Zhang et al., 2018b), or eventual post article score (Rangwala and Jamali, 2010; Szabo and Huberman, 2010),; but all where, like us,”
Sociology in the Era of Big Data: The Ascent of Forensic Social Science (2015, unknown): “In short, we now have the means to discern what makes for better or worse outcomes in political dialogues, where “ better ” or “ worse ” can be defined in any number of ways (for example, see Backstrom et al. 2013).”