Top Comment or Flop Comment? Predicting and Explaining User Engagement in Online News Discussions

Risch, Julian; Krestel, Ralf

doi:10.1609/icwsm.v14i1.7325

Cited by 11 publications

(10 citation statements)

References 45 publications

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“…A large body of work has examined the role of moderation interventions such explanations , content removal (Chandrasekharan et al 2018), community bans (Chandrasekharan et al 2017, 2020Saleem and Ruths 2018) on outcomes such as migration (Ribeiro et al 2020), harassment (Matias 2019a) and harmful language use (Wadden et al 2021). Others have focused on moderators themselves (Matias 2019b;Dosono and Semaan 2019), and technological tools to assist them Zhang, Hugh, and Bernstein 2020;Chandrasekharan et al 2019), as well as self-moderation through voting (Glenski, Pennycuff, and Weninger 2017;Risch and Krestel 2020) and community norms . In contrast, our work informs the viability of different moderation strategies, specifically by examining the sharing and visibility of news content across thousands of communities.…”

Section: Moderation and Governancementioning

confidence: 99%

“…While previous research has led to important insights on specific aspects of news sharing, such as user engagement (Risch and Krestel 2020), fact checking (Vosoughi, Roy, and Aral 2018;Choi et al 2020), specific communities (Rajadesingan, Resnick, and Budak 2020), and specific rumors (Vosoughi, Mohsenvand, and Roy 2017;Qazvinian et al 2011), large scale studies of news sharing are critical to understanding polarization and misinformation more broadly, and can inform community design, governance, and moderation interventions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Political Bias and Factualness in News Sharing across more than 100,000 Online Communities

Weld

Glenski

Althoff

2021

ICWSM

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As civil discourse increasingly takes place online, misinformation and the polarization of news shared in online communities have become ever more relevant concerns with real world harms across our society. Studying online news sharing at scale is challenging due to the massive volume of content which is shared by millions of users across thousands of communities. Therefore, existing research has largely focused on specific communities or specific interventions, such as bans. However, understanding the prevalence and spread of misinformation and polarization more broadly, across thousands of online communities, is critical for the development of governance strategies, interventions, and community design. Here, we conduct the largest study of news sharing on reddit to date, analyzing more than 550 million links spanning 4 years. We use non-partisan news source ratings from Media Bias/Fact Check to annotate links to news sources with their political bias and factualness. We find that, compared to left-leaning communities, right-leaning communities have 105% more variance in the political bias of their news sources, and more links to relatively-more biased sources, on average. We observe that reddit users’ voting and re-sharing behaviors generally decrease the visibility of extremely biased and low factual content, which receives 20% fewer upvotes and 30% fewer exposures from crossposts than more neutral or more factual content. This suggests that reddit is more resilient to low factual content than Twitter. We show that extremely biased and low factual content is very concentrated, with 99% of such content being shared in only 0.5% of communities, giving credence to the recent strategy of community-wide bans and quarantines.

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Section: Moderation and Governancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Political Bias and Factualness in News Sharing across more than 100,000 Online Communities

Weld

Glenski

Althoff

2021

ICWSM

View full text Add to dashboard Cite

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“…Our findings also show that the expression types linked to positive recovery outcomes (ie, emotional support giving and universality expressions) increased over time as a proportion of messages sent, suggesting the potential importance of sustaining participation and motivating more participants to interact. Often, as in the case of any online discussion forum, peer-to-peer forums face a cold start problem , wherein early contributions to a forum generally receive little engagement; yet, without engagement, other participants are unlikely to participate [ 95 ]. This has led to strategies such as seeding a group with highly motivated participants to create a sense of community, given that participation is based on anticipated future interaction from group members [ 35 , 96 ].…”

Section: Discussionmentioning

confidence: 99%

Using Machine Learning of Online Expression to Explain Recovery Trajectories: Content Analytic Approach to Studying a Substance Use Disorder Forum

Yang¹,

Kornfield²,

Liu³

et al. 2023

J Med Internet Res

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Background Smartphone-based apps are increasingly used to prevent relapse among those with substance use disorders (SUDs). These systems collect a wealth of data from participants, including the content of messages exchanged in peer-to-peer support forums. How individuals self-disclose and exchange social support in these forums may provide insight into their recovery course, but a manual review of a large corpus of text by human coders is inefficient. Objective The study sought to evaluate the feasibility of applying supervised machine learning (ML) to perform large-scale content analysis of an online peer-to-peer discussion forum. Machine-coded data were also used to understand how communication styles relate to writers’ substance use and well-being outcomes. Methods Data were collected from a smartphone app that connects patients with SUDs to online peer support via a discussion forum. Overall, 268 adult patients with SUD diagnoses were recruited from 3 federally qualified health centers in the United States beginning in 2014. Two waves of survey data were collected to measure demographic characteristics and study outcomes: at baseline (before accessing the app) and after 6 months of using the app. Messages were downloaded from the peer-to-peer forum and subjected to manual content analysis. These data were used to train supervised ML algorithms using features extracted from the Linguistic Inquiry and Word Count (LIWC) system to automatically identify the types of expression relevant to peer-to-peer support. Regression analyses examined how each expression type was associated with recovery outcomes. Results Our manual content analysis identified 7 expression types relevant to the recovery process (emotional support, informational support, negative affect, change talk, insightful disclosure, gratitude, and universality disclosure). Over 6 months of app use, 86.2% (231/268) of participants posted on the app’s support forum. Of these participants, 93.5% (216/231) posted at least 1 message in the content categories of interest, generating 10,503 messages. Supervised ML algorithms were trained on the hand-coded data, achieving F1-scores ranging from 0.57 to 0.85. Regression analyses revealed that a greater proportion of the messages giving emotional support to peers was related to reduced substance use. For self-disclosure, a greater proportion of the messages expressing universality was related to improved quality of life, whereas a greater proportion of the negative affect expressions was negatively related to quality of life and mood. Conclusions This study highlights a method of natural language processing with potential to provide real-time insights into peer-to-peer communication dynamics. First, we found that our ML approach allowed for large-scale content coding while retaining moderate-to-high levels of accuracy. Second, individuals’ expression styles were associated with recovery outcomes. The expression types of emotional support, universality disclosure, and negative affect were significantly related to recovery outcomes, and attending to these dynamics may be important for appropriate intervention.

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“…In the 1990s of the 20th century, newspapers identified readers engagement by the hand‐written letters to the editor (Risch & Krestel, 2020). With the establishment of online platforms, reader engagement definition evolved, and reader engagement has been measured in several different ways.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…With the establishment of online platforms, reader engagement definition evolved, and reader engagement has been measured in several different ways. From the number of comments (Risch & Krestel, 2020) to a wide range of digital metrics, such as visits or time spent reading (Moyo et al, 2019). Furthermore, consumer engagement involves emotional attachment and rational loyalty (Barari et al, 2021; Lagun & Lalmas, 2016; Riskos et al, 2022; Wenzel et al, 2022).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A data‐driven approach to improve online consumer subscriptions by combining data visualization and machine learning methods

Fernandes,

Moro,

Cortez

2024

Int J Consumer Studies

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Effective online consumer research helps companies on defining a successful strategy to increase user loyalty and shape brand engagement. Digital innovation introduced a dramatic change in businesses, particularly in the online news industry. Content consumers have a wide offer across different channels which increase the digital challenge for online news media companies to retain their readers and convert them into online subscribers. Furthermore, digital news publishers often strive to balance revenue sources in online business models. Thus, this study fills a gap in the literature on media consumer research by proposing a data‐driven approach that combines two machine learning (ML) models to allow managers dynamically improve their marketing and editorial strategies. Firstly, the authors present an online user profiling to identify consumer segments based on the interplay between several engagement’ variables substantiated in the literature research. Second, as few studies have explored the factors influencing users' intention to pay for such services, the eXtreme Gradient Boosting ML algorithm identifies the predictors of consumer's willingness to pay. Third, a dashboard presents the key performance indicators across the audience funnel. Thus, practical implications and business suggestions are presented in a two‐fold strategy to maximize revenue from digital subscriptions and advertising. Findings provide new insights into an engagement approach and the relation to acquire a digital subscription in online content platforms. We believe that the provided recommendations are potentially useful to help marketing and editorial teams to manage their customer engagement process across the funnel in a more efficient way.

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Top Comment or Flop Comment? Predicting and Explaining User Engagement in Online News Discussions

Cited by 11 publications

References 45 publications

Political Bias and Factualness in News Sharing across more than 100,000 Online Communities

Political Bias and Factualness in News Sharing across more than 100,000 Online Communities

Using Machine Learning of Online Expression to Explain Recovery Trajectories: Content Analytic Approach to Studying a Substance Use Disorder Forum

A data‐driven approach to improve online consumer subscriptions by combining data visualization and machine learning methods

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