Dataset Information

Understanding who talks about what: comparison between the information treatment in traditional media and online discussions.

ABSTRACT: We study the dynamics of interactions between a traditional medium, the New York Times journal, and its followers in Twitter, using a massive dataset. It consists of the metadata of the articles published by the journal during the first year of the COVID-19 pandemic, and the posts published in Twitter by a large set of followers of the @nytimes account along with those published by a set of followers of several other media of different kind. The dynamics of discussions held in Twitter by exclusive followers of a medium show a strong dependence on the medium they follow: the followers of @FoxNews show the highest similarity to each other and a strong differentiation of interests with the general group. Our results also reveal the difference in the attention payed to U.S. presidential elections by the journal and by its followers, and show that the topic related to the "Black Lives Matter" movement started in Twitter, and was addressed later by the journal.

SUBMITTER: Schawe H

PROVIDER: S-EPMC9990029 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Understanding who talks about what: comparison between the information treatment in traditional media and online discussions.

Schawe Hendrik H Beiró Mariano G MG Alvarez-Hamelin J Ignacio JI Kotzinos Dimitris D Hernández Laura L

Scientific reports 20230307 1

We study the dynamics of interactions between a traditional medium, the New York Times journal, and its followers in Twitter, using a massive dataset. It consists of the metadata of the articles published by the journal during the first year of the COVID-19 pandemic, and the posts published in Twitter by a large set of followers of the @nytimes account along with those published by a set of followers of several other media of different kind. The dynamics of discussions held in Twitter by exclusi ...[more]

PMID: 36882458

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Project description:BackgroundMaintaining a healthy weight can reduce the risk of developing many diseases, including type 2 diabetes, hypertension, and certain types of cancers. Online social media platforms are popular among people seeking social support regarding weight loss and sharing their weight loss experiences, which provides opportunities for learning about weight loss behaviors.ObjectiveThis study aimed to investigate the extent to which the content posted by users in the r/loseit subreddit, an online community for discussing weight loss, and online interactions were associated with their weight loss in terms of the number of replies and votes that these users received.MethodsAll posts that were published before January 2018 in r/loseit were collected. We focused on users who revealed their start weight, current weight, and goal weight and were active in this online community for at least 30 days. A topic modeling technique and a hierarchical clustering algorithm were used to obtain both global topics and local word semantic clusters. Finally, we used a regression model to learn the association between weight loss and topics, word semantic clusters, and online interactions.ResultsOur data comprised 477,904 posts that were published by 7660 users within a span of 7 years. We identified 25 topics, including food and drinks, calories, exercises, family members and friends, and communication. Our results showed that the start weight (β=.823; P<.001), active days (β=.017; P=.009), and median number of votes (β=.263; P=.02), mentions of exercises (β=.145; P<.001), and nutrition (β=.120; P<.001) were associated with higher weight loss. Users who lost more weight might be motivated by the negative emotions (β=-.098; P<.001) that they experienced before starting the journey of weight loss. In contrast, users who mentioned vacations (β=-.108; P=.005) and payments (β=-.112; P=.001) tended to experience relatively less weight loss. Mentions of family members (β=-.031; P=.03) and employment status (β=-.041; P=.03) were associated with less weight loss as well.ConclusionsOur study showed that both online interactions and offline activities were associated with weight loss, suggesting that future interventions based on existing online platforms should focus on both aspects. Our findings suggest that online personal health data can be used to learn about health-related behaviors effectively.

Dataset Information

Understanding who talks about what: comparison between the information treatment in traditional media and online discussions.

Publications

Understanding who talks about what: comparison between the information treatment in traditional media and online discussions.

Similar Datasets

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