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1. On the dynamics of user engagement in news comment media

   Lihong He, Chao Han ( November 2019 , Wiley interdisciplinary reviews)

   Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, that is, comments. A news outlet may allow users to contribute comments on all their articles or a selected number of them. The topic of an article may lead to an apathetic user commenting activity (several tens of comments) or to a spontaneous fervent one (several thousands of comments). This environment creates a social dynamic that is little studied. The social dynamics around articles have the potential to reveal interesting facets of the user population at a news outlet. In this paper, we report the salient findings about these social media from 15 months worth of data collected from 17 news outlets comprising of over 38,000 news articles and about 21 million user comments. Analysis of the data reveals interesting insights such as there is an uneven relationship between news outlets and their user populations across outlets. Such observations and others have not been revealed, to our knowledge. We believe our analysis in this paper can contribute to news predictive analytics (e.g., user reaction to a news article or predicting the volume of comments posted to an article). 

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2. On the dynamics of user engagement in news comment media

   https://doi.org/10.1002/widm.1342  

   He, Lihong ; Han, Chao ; Mukherjee, Arjun ; Obradovic, Zoran ; Dragut, Eduard ( November 2019 , WIREs Data Mining and Knowledge Discovery)

   Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, that is, comments. A news outlet may allow users to contribute comments on all their articles or a selected number of them. The topic of an article may lead to an apathetic user commenting activity (several tens of comments) or to a spontaneous fervent one (several thousands of comments). This environment creates a social dynamic that is little studied. The social dynamics around articles have the potential to reveal interesting facets of the user population at a news outlet. In this paper, we report the salient findings about these social media from 15 months worth of data collected from 17 news outlets comprising of over 38,000 news articles and about 21 million user comments. Analysis of the data reveals interesting insights such as there is an uneven relationship between news outlets and their user populations across outlets. Such observations and others have not been revealed, to our knowledge. We believe our analysis in this paper can contribute to news predictive analytics (e.g., user reaction to a news article or predicting the volume of comments posted to an article).

   This article is categorized under:

   Internet > Society and Culture

   Ensemble Methods > Web Mining

   Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction

    

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3. Cannot Predict Comment Volume of a News Article before (a few) Users Read It

   Lihong He, Chen Shen ( January 2021 , Proceedings of the International AAAI Conference on Weblogs and Social Media)

   null (Ed.)

   Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, i.e., comments. An article may attract an apathetic user engagement (several tens of comments) or a spontaneous fervent user engagement (thousands of comments). In this paper, we study the problem of predicting the total number of user comments a news article will receive. Our main insight is that the early dynamics of user comments contribute the most to an accurate prediction, while news article specific factors have surprisingly little influence. This appears to be an interesting and understudied phenomenon: collective social behavior at a news outlet shapes user response and may even downplay the content of an article. We compile and analyze a large number of features, both old and novel from literature. The features span a broad spectrum of facets including news article and comment contents, temporal dynamics, sentiment/linguistic features, and user behaviors. We show that the early arrival rate of comments is the best indicator of the eventual number of comments. We conduct an in-depth analysis of this feature across several dimensions, such as news outlets and news article categories. We show that the relationship between the early rate and the final number of comments as well as the prediction accuracy vary considerably across news outlets and news article categories (e.g., politics, sports, or health). 

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4. Predicting Facebook sentiments towards research

   https://doi.org/10.1016/j.nlp.2023.100010  

   Shahzad, Murtuza ; Freeman, Cole ; Rahimi, Mona ; Alhoori, Hamed ( June 2023 , Natural Language Processing Journal)

   Social media platforms provide users with various ways of interacting with each other, such as commenting, reacting to posts, sharing content, and uploading pictures. Facebook is one of the most popular platforms, and its users frequently share and reshare posts, including research articles. Moreover, the reactions feature on Facebook allows users to express their feelings towards the content they view, providing valuable data for analysis. This study aims to predict the emotional impact of Facebook posts relating to research articles. We collected data on Facebook posts related to various scientific research domains, including Health Sciences, Social Sciences, Dentistry, Arts, and Humanities. We observed Facebook users’ reactions towards research articles and posts and found that ‘Like’ reactions were the most common. We also noticed that research articles from the Dentistry research domain received a lot of ‘Haha’ reactions. We used machine learning models to predict the sentiment of Facebook posts related to research articles. We used features such as the research article’s title sentiment, abstract sentiment, abstract length, author count, and research domain to build the models. We used five classifiers: Random Forest, Decision Tree, K-Nearest Neighbors, Logistic Regression, and Naïve Bayes. The models were evaluated using accuracy, precision, recall, and F-1 score metrics. The Random Forest classifier was the best model for two- and three-class labels, achieving accuracy measures of 86% and 66%, respectively. We also evaluated the feature importance for the Random Forest model and found that the sentiment of the research article’s title is crucial in predicting the sentiment of the Facebook post. This study has substantial implications for public engagement in science-related messages. The emotional reactions of Facebook users towards research articles and posts can provide valuable insights into public engagement in science, and predicting the emotional impact of Facebook posts related to research articles can help researchers understand how the public perceives scientific research. The findings of the study can aid researchers in effectively communicating their research and engaging the public in scientific discourse. 

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5. Teens’ Social Media Engagement during the COVID-19 Pandemic: A Time Series Examination of Posting and Emotion on Reddit

   https://doi.org/10.3390/ijerph181910079  

   Zhang, Saijun ; Liu, Meirong ; Li, Yeefay ; Chung, Jae Eun ( October 2021 , International Journal of Environmental Research and Public Health)

   Research has rarely examined how the COVID-19 pandemic may affect teens’ social media engagement and psychological wellbeing, and even less research has compared the difference between teens with and without mental health concerns. We collected and analyzed weekly data from January to December 2020 from teens in four Reddit communities (subreddits), including teens in r/Teenagers and teens who participated in three mental health subreddits (r/Depression, r/Anxiety, and r/SuicideWatch). The results showed that teens’ weekly subreddit participation, posting/commenting frequency, and emotion expression were related to significant pandemic events. Teen Redditors on r/Teenagers had a higher posting/commenting frequency but lower negative emotion than teen Redditors on the three mental health subreddits. When comparing posts/comments on r/Teenagers, teens who ever visited one of the three mental health subreddits posted/commented twice as frequently as teens who did not, but their emotion expression was similar. The results from the Interrupted Time Series Analysis (ITSA) indicated that both teens with and without mental health concerns reversed the trend in posting frequency and negative emotion from declining to increasing right after the pandemic outbreak, and teens with mental health concerns had a more rapidly increasing trend in posting/commenting. The findings suggest that teens’ social media engagement and emotion expression reflect the pandemic evolution. Teens with mental health concerns are more likely to reveal their emotions on specialized mental health subreddits rather than on the general r/Teenagers subreddit. In addition, the findings indicated that teens with mental health concerns had a strong social interaction desire that various barriers in the real world may inhibit. The findings call for more attention to understand the pandemic’s influence on teens by monitoring and analyzing social media data and offering adequate support to teens regarding their mental health wellbeing. 

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