Dataset Information

Who supports Bernie? Analyzing identity and ideological variation on Twitter during the 2020 democratic primaries.

ABSTRACT: Using a novel dataset of 590M messages by 21M users, we present the first large-scale examination of the behavior of likely Bernie supporters on Twitter during the 2020 U.S. Democratic primaries and presidential election. We use these data to dispel empirically the notion of a unified, stereotypical Bernie supporter (e.g., the "Bernie Bro"). Instead, our work uncovers significant variation in the identities and ideologies of Bernie supporters who were active on Twitter. Our work makes three contributions to the literature on social media and social movements. Methodologically, we present a novel mixed methods approach to surface identity and ideological variation within a movement via use of patterns in who retweets whom (i.e. who retweets which other users) and who retweets what (i.e. who retweets which specific tweets). Substantively, documentation of these variations challenges a trend in the social movement literature to assume actors within a particular movement are unified in their ideology, identity, and values.

SUBMITTER: Shuster SM

PROVIDER: S-EPMC11008827 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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Who supports Bernie? Analyzing identity and ideological variation on Twitter during the 2020 democratic primaries.

Shuster Stef M SM Campos-Castillo Celeste C Madani Navid N Joseph Kenneth K

PloS one 20240411 4

Using a novel dataset of 590M messages by 21M users, we present the first large-scale examination of the behavior of likely Bernie supporters on Twitter during the 2020 U.S. Democratic primaries and presidential election. We use these data to dispel empirically the notion of a unified, stereotypical Bernie supporter (e.g., the "Bernie Bro"). Instead, our work uncovers significant variation in the identities and ideologies of Bernie supporters who were active on Twitter. Our work makes three cont ...[more]

PMID: 38603640

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Project description:Public concern regarding health systems has experienced a rapid surge during the last two years due to the COVID-19 outbreak. Accordingly, medical professionals and health-related institutions reach out to patients and seek feedback to analyze, monitor, and uplift medical services. Such views and perceptions are often shared on social media platforms like Facebook, Instagram, Twitter, etc. Twitter is the most popular and commonly used by the researcher as an online platform for instant access to real-time news, opinions, and discussion. Its trending hashtags (#) and viral content make it an ideal hub for monitoring public opinion on a variety of topics. The tweets are extracted using three hashtags #healthcare, #healthcare services, and #medical facilities. Also, location and tweet sentiment analysis are considered in this study. Several recent studies deployed Twitter datasets using ML and DL models, but the results show lower accuracy. In addition, the studies did not perform extensive comparative analysis and lack validation. This study addresses two research questions: first, what are the sentiments of people toward medical services worldwide? and second, how effective are the machine learning and deep learning approaches for the classification of sentiment on healthcare tweets? Experiments are performed using several well-known machine learning models including support vector machine, logistic regression, Gaussian naive Bayes, extra tree classifier, k nearest neighbor, random forest, decision tree, and AdaBoost. In addition, this study proposes a transfer learning-based LSTM-ETC model that effectively predicts the customer's satisfaction level from the healthcare dataset. Results indicate that despite the best performance by the ETC model with an 0.88 accuracy score, the proposed model outperforms with a 0.95 accuracy score. Predominantly, the people are happy about the provided medical services as the ratio of the positive sentiments is substantially higher than the negative sentiments. The sentiments, either positive or negative, play a crucial role in making important decisions through customer feedback and enhancing quality.

Dataset Information

Who supports Bernie? Analyzing identity and ideological variation on Twitter during the 2020 democratic primaries.

Publications

Who supports Bernie? Analyzing identity and ideological variation on Twitter during the 2020 democratic primaries.

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