A time-robust group recommender for featured comments on news platforms

Introduction

Recently, content moderators on news platforms face the challenging task to select high-quality comments to feature on the webpage, a manual and time-consuming task exacerbated by platform growth. This paper introduces a group recommender system based on classifiers to aid moderators in this selection process.

Methods

Utilizing data from a Dutch news platform, we demonstrate that integrating comment data with user history and contextual relevance yields high ranking scores. To evaluate our models, we created realistic evaluation scenarios based on unseen online discussions from both 2020 and 2023, replicating changing news cycles and platform growth.

Results

We demonstrate that our best-performing models maintain their ranking performance even when article topics change, achieving an optimum mean NDCG@5 of 0.89.

Discussion

The expert evaluation by platform-employed moderators underscores the subjectivity inherent in moderation practices, emphasizing the value of recommending comments over classification. Our research contributes to the advancement of (semi-)automated content moderation and the understanding of deliberation quality assessment in online discourse.

Debate: Social media content moderation may do more harm than good for youth mental health

Most social media platforms censor and moderate content related to mental illness to protect users from harm, though this may be at the expense of potential positive outcomes for youth mental health. Current evidence does not offer strong support for the relationship between censoring mental health content and preventing harm. In fact, existing moderation strategies can perpetuate negative consequences for mental health by creating isolated and polarized communities where at-risk youth remain exposed to harmful content, such as pro-eating disorder communities that use lexical variants to evade censorship. Social media censorship of content related to mental illness can also silence positive discourse about mental health, create barriers to accessing online support and resources, and hinder research efforts on youth well-being. Social media content about mental health can have important positive impacts on youth mental health by facilitating help-seeking, depicting positive coping strategies, and promoting a sense of belonging for struggling youth, but these benefits are minimized under existing moderation and censorship practices. This article presents a call to action for evidence-based social media policies and for practitioners to consider the clinical implications of social media engagement when connecting with young patients.

The nudging effect of AIGC labeling on users' perceptions of automated news: evidence from EEG

Introduction

In the context of generative AI intervention in news production, this study primarily focuses on the impact of AI-generated content (AIGC) labeling cues on users' perceptions of automated news based on nudge theory.

Methods

A 2 (authorship disclosure nudge cues: with vs. without AIGC label) × 2 (automated news type: descriptive vs. evaluative news) within-subject experiment was carried out. Thirty-two participants were recruited to read automated news, evaluate the perceived content trustworthiness, and record with an EEG device.

Results

The results demonstrated that disclosure of AIGC labeling significantly reduced the trustworthiness perception of both fact-based descriptive and opinion-based evaluative news. In EEG, the delta PSD, theta PSD, alpha PSD, and beta PSD with disclosure of AIGC labeling were significantly higher than those without AIGC labeling. Meanwhile, in descriptive news conditions, TAR with AIGC labeling was higher than without AIGC labeling.

Discussion

These results suggested that AIGC labeling significantly improves the degree of attention concentration in reading and deepens the degree of cognitive processing. Users are nudged by AIGC labeling to shift their limited attention and cognitive resources to re-evaluate the information quality to obtain more prudent judgment results. This helps to supplement the theoretical perspective on transparent disclosure nudging in the Internet content governance research field, and it can offer practical guidance to use content labeling to regulate the media industry landscape in the face of AI's pervasive presence.

Misinformation warning labels are widely effective: A review of warning effects and their moderating features

There is growing concern over the spread of misinformation online. One widely adopted intervention by platforms for addressing falsehoods is applying "warning labels" to posts deemed inaccurate by fact-checkers. Despite a rich literature on correcting misinformation after exposure, much less work has examined the effectiveness of warning labels presented concurrent with exposure. Promisingly, existing research suggests that warning labels effectively reduce belief and spread of misinformation. The size of these beneficial effects depends on how the labels are implemented and the characteristics of the content being labeled. Despite some individual differences, recent evidence indicates that warning labels are generally effective across party lines and other demographic characteristics. We discuss potential implications and limitations of labeling policies for addressing online misinformation.

Community-oriented Motivational Interviewing (MI): A novel framework extending MI to address COVID-19 vaccine misinformation in online social media platforms

Researchers have linked circulating misinformation in online platforms to low COVID-19 vaccine uptake. Two disparate literatures provide relevant initial guidance to address the problem. Motivational Interviewing (MI) effectively reduces vaccine hesitancy in clinical environments; meanwhile, social scientists note inoculation, rebuttal, and appeals to accuracy are persuasive in digital contexts. A tension is inherent in these approaches. MI in digital forums may induce an 'illusory truth effect,' wherein falsehoods appear more accurate through repetition. Yet, rebutting misinformation directly may elicit backfire or reactance effects, motivating some to amplify their presentation of misinformation. Building on Identity Process Theory, we propose a theoretical framework for conducting MI-based infodemiology interventions among digital communities that conceptualizes the community in toto (rather than one specific person) as the unit of focus. Case examples from interventions on public Facebook posts illustrate three processes unique to such interventions: 1) Navigating tension between addressing commenters and "bystanders"; 2) Activating pro-vaccine bystanders; and 3) Reframing uncertainty or information individuals might find concerning or threatening according to implied collective values. This paper suggests community-oriented MI can maximize persuasive effects on bystanders while minimizing potential reactance from those with committed beliefs, thereby guiding community-oriented public health messaging interventions enacted in digital environments.

Fake news reminders and veracity labels differentially benefit memory and belief accuracy for news headlines

Fake news exposure can negatively affect memory and beliefs, thus sparking debate about whether to repeat misinformation during corrections. The once-prevailing view was that repeating misinformation increases its believability and should thus be avoided. However, misinformation reminders have more recently been shown to enhance memory and belief accuracy. We replicated such reminder benefits in two experiments using news headlines and compared those benefits against the effects of veracity labeling. Specifically, we examined the effects of labeling real news corrections to enhance conflict salience (Experiment 1) and labeling fake news on its debut to encourage intentional forgetting (Experiment 2). Participants first viewed real and fake news headlines with some fake news labeled as false. Participants then saw labeled and unlabeled real news corrections; labeled corrections appeared alone or after fake news reminders. Reminders promoted the best memory and belief accuracy, whereas veracity labels had selective effects. Correction labels led to intermediate memory and belief accuracy, whereas fake news labels improved accuracy for beliefs more than memory. The extent that real and fake news details were recalled together correlated with overall memory and belief differences across conditions, implicating a critical role for integrative encoding that was promoted most by fake news reminders.

How Do Monetary Incentives Affect Healthcare Social Media Content? A Study Based on Topic Modeling and Sentiment Analysis (Preprint)

BACKGROUND

While there is high-quality online health information, a lot of recent work has unfortunately highlighted significant issues with the health content on social media platforms (eg, fake news and misinformation), the consequences of which are severe in health care. One solution is to investigate methods that encourage users to post high-quality content.

OBJECTIVE

Incentives have been shown to work in many domains, but until recently, there was no method to provide financial incentives easily on social media for users to generate high-quality content. This study investigates the following question: What effect does the provision of incentives have on the creation of social media health care content?

METHODS

We analyzed 8328 health-related posts from an incentive-based platform (Steemit) and 1682 health-related posts from a traditional platform (Reddit). Using topic modeling and sentiment analysis-based methods in machine learning, we analyzed these posts across the following 3 dimensions: (1) emotion and language style using the IBM Watson Tone Analyzer service, (2) topic similarity and difference from contrastive topic modeling, and (3) the extent to which posts resemble clickbait. We also conducted a survey using 276 Amazon Mechanical Turk (MTurk) users and asked them to score the quality of Steemit and Reddit posts.

RESULTS

Using the Watson Tone Analyzer in a sample of 2000 posts from Steemit and Reddit, we found that more than double the number of Steemit posts had a confident language style compared with Reddit posts (77 vs 30). Moreover, 50% more Steemit posts had analytical content and 33% less Steemit posts had a tentative language style compared with Reddit posts (619 vs 430 and 416 vs 627, respectively). Furthermore, more than double the number of Steemit posts were considered joyful compared with Reddit posts (435 vs 200), whereas negative posts (eg, sadness, fear, and anger) were 33% less on Steemit than on Reddit (384 vs 569). Contrastive topic discovery showed that only 20% (2/10) of topics were common, and Steemit had more unique topics than Reddit (5 vs 3). Qualitatively, Steemit topics were more informational, while Reddit topics involved discussions, which may explain some of the quantitative differences. Manual labeling marked more Steemit headlines as clickbait than Reddit headlines (66 vs 26), and machine learning model labeling consistently identified a higher percentage of Steemit headlines as clickbait than Reddit headlines. In the survey, MTurk users said that at least 57% of Steemit posts had better quality than Reddit posts, and they were at least 52% more likely to like and comment on Steemit posts than Reddit posts.

CONCLUSIONS

It is becoming increasingly important to ensure high-quality health content on social media; therefore, incentive-based social media could be important in the design of next-generation social platforms for health information.

Trust in COVID-19 public health information

Understanding the factors that influence trust in public health information is critical for designing successful public health campaigns during pandemics such as COVID-19. We present findings from a cross-sectional survey of 454 US adults-243 older (65+) and 211 younger (18-64) adults-who responded to questionnaires on human values, trust in COVID-19 information sources, attention to information quality, self-efficacy, and factual knowledge about COVID-19. Path analysis showed that trust in direct personal contacts (B = 0.071, p = .04) and attention to information quality (B = 0.251, p < .001) were positively related to self-efficacy for coping with COVID-19. The human value of self-transcendence, which emphasizes valuing others as equals and being concerned with their welfare, had significant positive indirect effects on self-efficacy in coping with COVID-19 (mediated by attention to information quality; effect = 0.049, 95% CI 0.001-0.104) and factual knowledge about COVID-19 (also mediated by attention to information quality; effect = 0.037, 95% CI 0.003-0.089). Our path model offers guidance for fine-tuning strategies for effective public health messaging and serves as a basis for further research to better understand the societal impact of COVID-19 and other public health crises.