No country for old members: user lifecycle and linguistic change in online communities

By Cristian Danescu-Niculescu-Mizil, Robert West, Dan Jurafsky, Jure Leskovec, Christopher Potts. 2013

440 citations recorded by the source corpus.

Subfields: Communication, Human-Computer Interaction

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Citations in the corpus

From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews (2013, background): “We mention the parallel work of [9], which also studies BeerAdvocate and RateBeer data: there, a user’s failure to adopt the linguistic norms of a community is considered as a factor that may inﬂuence whether they will abandon that community.”
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “, trends in the use of language) and the online communities’ makeup and users’ lifecycles [Danescu-Niculescu-Mizil et al. 2013].”
Averaging Gone Wrong: Using Time-Aware Analyses to Better Understand Behavior (2016, background): “Some adopt a point of view of the whole network and try to understand general patterns of behavior [33, 71], while others adopt a user-centric point of view and try to model [10,48,52,68] or predict [12] individuals’ behavior.”
Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview (2019, background): “(2013); Danescu-Niculescu-Mizil et al. (2013); Hovy (2018); Yang et al. (2019). Considering the author demographics improves the accuracy of text classifiersVolkova et al.”
Antisocial Behavior in Online Discussion Communities (2015, background): “Users can stay on-topic or veer off-topic; prior work has also shown that users tend to adopt linguistic conventions or jargon in a community (Danescu-Niculescu-Mizil et al. 2013) and that they also unconsciously mimic the choices of function-word classes they are communicating with…”
Quick, Community-Specific Learning: How Distinctive Toxicity Norms Are Maintained in Political Subreddits (2020, background): “This phenomenon—learning by participation—has been well studied in online communities (Lampe and Johnston 2005; Danescu-Niculescu-Mizil et al. 2013).”
The Importance of Modeling Social Factors of Language: Theory and Practice (2021, background): “They go a long way to explaining other socially motivated constructs, such as power imbalances or politeness, which in turn can also be inferred from dialogue (Prabhakaran et al., 2012; Danescu-Niculescu-Mizil et al., 2013a).”
Third wave positive psychology: broadening towards complexity (2020, background): “Beyond that, analysis of language has included research on: the effects of power differentials on community engagement (Danescu-Niculescu-Mizil et al., 2013); personal values (Boyd et al., 2015); emotions (Bollen et al., 2011; Strapparava & Mihalcea, 2008); and psychological traits (Guntuku et al.,…”
Characterizing the Language of Online Communities and its Relation to Community Reception (2016, background): “those of the community they participate in, reaching “linguistic maturity” over time (Nguyen and Rosé, 2011; Danescu-Niculescu-Mizil et al., 2013).”
Recurrent Recommender Networks (2017, background): “This is well established in online communities [8] and it arguably also applies to online consumption.”
The Enemy in Your Own Camp: How Well Can We Detect Statistically-Generated Fake Reviews – An Adversarial Study (2016, background): “Reviews are a rich source of studies for NLP, and a variety of recent papers (McAuley et al., 2012; Danescu-Niculescu-Mizil et al., 2013; Reschke et al., 2013; Jurafsky et al., 2014; Hovy et al., 2015) have explored it.”
Of Pins and Tweets: Investigating How Users Behave Across Image- and Text-Based Social Networks (2014, background): “Prior work has observed similar linguistic adaption on online forums (Danescu-Niculescu-Mizil et al. 2013).”
All Who Wander: On the Prevalence and Characteristics of Multi-community Engagement (2015, methodology): “Since we need our users’ community trajectories to be long enough to be able to exhibit significant wandering (whether or not they actually do), the set of users we consider are those who have made at least 50 posts, following the choice in [9].”
Semantic Variation in Online Communities of Practice (2018, positive): “Yet other related work has focused on how acceptance into existing communities is mediated by the adoption of community norms (Nguyen and Rosé, 2011; Tran and Ostendorf, 2016) and on how the process whereby linguistic innovations become norms can be leveraged to predict the permanence of a user in a”
Neural Survival Recommender (2017, background): “First and foremost, the goal of any service is to retain its users and to grow its network [25, 6].”
Online Human-Bot Interactions: Detection, Estimation, and Characterization (2017, background): “Many recent papers have demonstrated the importance of content and language features in revealing the nature of social media conversations (Danescu-Niculescu-Mizil et al. 2013; McAuley and Leskovec 2013; Mocanu et al. 2013; Botta, Moat, and Preis 2015; Letchford, Moat, and Preis 2015; Das et al. 201”
Temporal Effects on Pre-trained Models for Language Processing Tasks (2021, methodology): “For longer time periods, a robust body of computational work has proposed methods for modeling the changes in active vocabulary (Dury and Drouin, 2011; Danescu-Niculescu-Mizil et al., 2013) and in meaning of words (Wijaya and Yeniterzi, 2011; Hamilton et al.”
Write Like I Write: Herding in the Language of Online Reviews (2014, background): “, a newcomer in the process of acquiring a group’s vocabulary versus an older participant who has stopped adjusting to group norms and may soon cease participating (Danescu-Niculescu-Mizil et al. 2013).”
Computational Sociolinguistics: A Survey (2016, methodology): “Review sites, such as TripAdvisor (Michael and Otterbacher 2014), IMDb (Hemphill and Otterbacher 2012) and beer review communities (Danescu-Niculescu-Mizil et al. 2013b), have also been used in studies on language in online communities.”