Modern election campaigns leverage social media and the networks within to get their messages directly out to the public. We use the theory of extended party networks to explore networks of engaged users who extensively amplify messages posted by political candidates. Using Twitter data from the Senate races in the U.S. 2018 midterm election, we build Twitter extended party networks out of dedicated amplifiers to understand how those engaged users associate with the candidate message amplification. Results show that certain super-amplifiers have a disproportionately large impact on information flows, and that decentralized networks with higher rates of follower reciprocity are associated with higher rates of message diffusion. Our study also finds some support for the idea that super-amplifiers engage in coordinated efforts to diffuse political messages.
PurposeInformation scientists may find value in studying cultural information evolution and information diffusion through memetics. Information studies in memetics have often found datafication in memetics research difficult. Meanwhile, current memetic scholarship elsewhere is abundant in data due to their focus on Internet artifacts. This paper offers a way to close the datafication gap for information researchers by associating information data with “differences” between memetic documents.Design/methodology/approachThis work offers a joint theory and methodology invested in information-oriented memetics. This methodology of differences is developed from a content analysis of difference on a collection of images with visual similarities.FindingsThe authors find that this kind of analysis provides a heuristic method for quantitatively bounding where one meme ends and another begins. The authors also find that this approach helps describe the dynamics of memetic media in such a way that the authors can datafy information or cultural evolution more clearly.Originality/valueHere the authors offer an approach for studying cultural information evolution through the study of memes. In doing so, the authors forward a methodology of difference which leverages content analysis in order to outline how it functions practically. The authors propose a quantitative methodology to assess differences between versions of document contents in order to examine what a particular meme is. The authors also move toward showing the information structure which defines a meme.
Science is increasingly carried out through scientific collaborations, allowing researchers pool their experience, knowledge, and skills. In this work we identify factors related to a scientist’s collaboration capacity, their ability accumulate new collaborations over their career. To do this offer a new collaboration capacity framework and begin the work of validating it empirically by testing a number of hypotheses. We use data from GenBank, a cyberinfrastructure (CI)‐enabled data repository that stores and manages scientific data. The data allow us to construct longitudinal networks, thereby giving us yearly scientific collaboration maps. We find that a scientist’s network position at an early stage is related to their capacity to build new collaborations and that researchers who manage an upward trend in productivity tend to have higher collaboration capacity. Our work makes a contribution to science of science studies by offering a collaboration capacity framework and providing partial empirical support for it.
As campaigns use social media to communicate with the public, this study investigates the dynamics of issue and image construction by the U.S. presidential candidates during the primary stage of the 2016 presidential campaign. Using computational techniques to classify candidate posts by message type and topic, we study all posts by the 17 Republican and 5 Democratic candidates from Sept. 1, 2015 through March 31, 2016. We ask whether candidates post more about their image--their character and personality--or on issues, and when they post on issues, does each candidate own specific policy issues. We also investigate which topics the public tends to engage with more. We also ask if there are differences in how the candidates use social media. Our results suggest that candidates post substantially less on the issues as compared with other types of messages. When they do post about policy topics, the candidates are associated with distinct policy positions, suggesting issue ownership as a strategic differentiator. Results also suggest campaigns use Facebook in ways different from Twitter, further reinforcing prior scholarship suggesting that campaigns use their social media for different purposes given different audiences on the platforms. Our findings indicate that campaigns overall are not discussing policy matters, thereby depriving the public the opportunity to engage in discussion of vital issues and what they would do to solve them; instead, the cult of personality seems to be further exacerbated by social media.
This study investigates the information sharing behavior of different levels of Twitter influencers within the context of the #BlackLivesMatter social movement and its related discussions #AlllivesMatter and #BlueLivesMatter during the 2-week period around Derek Chauvin’s trial. Using qualitative content analysis and quantitative machine learning methods, we analyzed over one million retweets to test if different levels of influencers tend to spread different kinds of information in the discussions around #All/Black/BlueLivesMatter on Twitter. We found out that different levels of influencers tend to spread different information within and between the #All/Black/BlueLivesMatter, and we offer some explanations through the lens of curation logics. We suggest that different levels of influencers may be exposed to different incentives, and be facing different social norms, which leads to different information behaviors. This research contributes to updating the theory of curated logics, virality, and influencers, as well as provides empirical data for the discussions of the #BlackLivesMatter social movement and its related discussions of #AllLivesMatter and #BlueLivesMatter.
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