Demographics of News Sharing in the U.S. Twittersphere

By Júlio Cesar dos Reis, Haewoon Kwak, Jisun An, Johnnatan Messias, Fabrício Benevenuto. 2017

34 citations recorded by the source corpus.

The widespread adoption and dissemination of online news through social media systems have been revolutionizing many segments of our society and ultimately our daily lives. In these systems, users can play a central role as they share content to their friends. Despite that, little is known about news spreaders in social media. In this paper, we provide the first of its kind in-depth characterization of news spreaders in social media. In particular, we investigate their demographics, what kind of content they share, and the audience they reach. Among our main findings, we show that males and white users tend to be more active in terms of sharing news, biasing the news audience to the interests of these demographic groups. Our results also quantify differences in interests of news sharing across demographics, which has implications for personalized news digests.

Subfields: Communication, Sociology and Political Science, Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

1. Jungseock Joo
2. Kimmo Kärkkäinen

In Sociology and Political Science

1. Kimmo Kärkkäinen
2. Jungseock Joo
3. Os Keyes

In Natural Language Processing

1. Jungseock Joo
2. Os Keyes

In Evaluation & Benchmarks

1. Jungseock Joo
2. Os Keyes
3. Kimmo Kärkkäinen
4. Yun Raymond Fu
5. Zaid Khan

Citations in the corpus

• FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age (2019, background): “Notable examples are demographic analyses of social media users using their photographs [9, 43, 64, 65, 62].”
• The Misgendering Machines (2018, background): “" [78] By specifying that three racial categories are "limited", and making no such specification for gender, the paper makes clear that gender is considered a binary.”
• FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation (2021, background): “Notable examples are demographic analyses of social media users using their photographs [9, 45, 65, 66, 63].”