Social media are utilized by millions of citizens to discuss important political issues. Politicians use these platforms to connect with the public and broadcast policy positions. Therefore, data from social media has enabled many studies of political discussion. While most analyses are limited to data from individual platforms, people are embedded in a larger information ecosystem spanning multiple social networks.Here we describe and provide access to the Indiana University 2022 U.S. Midterms Multi-Platform Social Media Dataset (MEIU22), a collection of social media posts from Twitter, Facebook, Instagram, Reddit, and 4chan. MEIU22 links to posts about the midterm elections based on a comprehensive list of keywords and tracks the social media accounts of 1,210 candidates from October 1 to December 25, 2022. We also publish the source code of our pipeline to enable similar multi-platform research projects.
Social media are utilized by millions of citizens to discuss important political issues. Politicians use these platforms to connect with the public and broadcast policy positions. Therefore, data from social media has enabled many studies of political discussion. While most analyses are limited to data from individual platforms, people are embedded in a larger information ecosystem spanning multiple social networks. Here we describe and provide access to the Indiana University 2022 U.S. Midterms Multi-Platform Social Media Dataset (MEIU22), a collection of social media posts from Twitter, Facebook, Instagram, Reddit, and 4chan. MEIU22 links to posts about the midterm elections based on a comprehensive list of keywords and tracks the social media accounts of 1,011 candidates from October 1 to December 25, 2022. We also publish the source code of our pipeline to enable similar multi-platform research projects.
Current practices of quantifying academic performance by productivity raise serious concerns about the psychological well-being of graduate students. These efforts often neglect the influence of researchers’ environment. Acknowledgments in dissertation subsections shed light on this environment by providing an opportunity for students to thank the people who supported them. We analysed 26,236 acknowledgments to create an “academic support network” that reveals five distinct communities that support students along the way: Academic, Administration, Family, Friends & Colleagues, and Spiritual. We show that female students mention fewer people from each of these communities, with the exception of their families, and that their productivity is slightly lower than that of males when considering the number of publications alone. This is critically important because it means that studying the doctoral process may help us better understand the adverse conditions women face early in their academic careers. Our results also suggest that the total number of people mentioned in the acknowledgements allows disciplines to be categorised as either individual science or team science as their magnitudes change. We also show that male students who mention more people from their academic community are associated with higher levels of productivity. University rankings are found to be positively correlated with productivity and the size of academic support networks. However, neither university rankings nor students’ productivity levels correlate with the sentiments students express in their acknowledgements. Our results point to the importance of academic support networks by explaining how they differ and how they influence productivity.
Current practices of quantifying performance by productivity leads serious concerns for psychological well-being of doctoral students and influence of research environment is often neglected in research evaluations. Acknowledgements in dissertations reflect the student experience and provide an opportunity to thank the people who support them. We conduct textual analysis of acknowledgments to build the "academic support network," uncovering five distinct communities: Academic, Administration, Family, Friends & Colleagues, and Spiritual; each of which is acknowledged differently by genders and disciplines. Female students mention fewer people from each community except for their families and total number of people mentioned in acknowledgements allows disciplines to be categorized as either individual science or team science. We also show that number of people mentioned from academic community is positively correlated with productivity and institutional rankings are found to be correlated with productivity and size of academic support networks but show no effect on students' sentiment on acknowledgements. Our results indicate the importance of academic support networks by explaining how they differ and how they influence productivity.
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