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This paper combines human- and computer-mediated content analysis to advance the understanding of how far-right parties are using new media to mobilize their supporters in the transitioning Ukrainian democracy. This study's theoretical approach employs the framework of connective action logic; results suggest that young and challenger parties are more adoptive of this logic, and provide opportunities for personalized engagement to their supporters. Moreover, use of interactive technologies by the studied parties also goes hand in hand with a smaller proportion of opinionated messages in their Twitter network, in spite of the inherent polarizing nature of these parties. Implications of these findings for research on future political campaigns conclude this paper. [ABSTRACT FROM AUTHOR]

The #overlyhonestmethods trend on Twitter is a space used by many scientists to peel back the curtain on their work and share observations and insights into the research world. We employ computer-assisted coding to assess the themes of 58,125 #overlyhonestmethods posts from January 7, 2013—the hashtag’s inception—to January 6, 2016. We additionally manually code a random sample of the census of tweets to evaluate the types of humor employed, as well as the targets of jokes and exclusivity of language. We offer analyses of this self-deprecating, insider conversation and an assessment of the associated ethical implications. [ABSTRACT FROM AUTHOR]

Using the "#arseniclife" controversy as a case study, we examine the roles of blogs and Twitter in post-publication review. The controversy was initiated by a scientific article about bacteria able to substitute arsenic for phosphorus in its genetic material. We present the debate chronologically, using prominent online media to reconstruct the events. Using tweets that discussed the controversy, we conducted quantitative sentiment analysis to examine skeptical and non-skeptical tones on Twitter. Critiques of and studies refuting the arsenic life hypothesis were publicized on blogs before formal publication in traditional academic spaces and were shared on Twitter, influencing issue salience among a range of audiences. This case exemplifies the role of new media in informal post-publication peer review, which can complement traditional peer review processes. The implications drawn from this case study for future conduct and transparency of both formal and informal peer review are discussed. [ABSTRACT FROM AUTHOR]

Recent technological developments have created novel opportunities for analyzing and identifying patterns in large volumes of digital content. However, many content analysis tools require researchers to choose between the validity of human-based coding and the ability to analyze large volumes of content through computer-based techniques. This study argues for the use of supervised content analysis tools that capitalize on the strengths of human- and computer-based coding for assessing opinion expression. We begin by outlining the key methodological issues surrounding content analysis as performed by human coders and existing computational algorithms. After reviewing the most popular analytic approaches, we introduce an alternative, hybrid method that is aimed at improving reliability, validity, and efficiency when analyzing social media content. To demonstrate the usefulness of this method, we track nuclear energy- and nanotechnology-related opinion expression on Twitter surrounding the Fukushima Daiichi accident to examine the extent to which the volume and tone of tweets shift in directions consistent with the expected external influence of the event. Our analysis revealed substantial shifts in both the volume and tone of nuclear power-related tweets that were consistent with our expectations following the disaster event. Conversely, there was decidedly more stability in the volume and tone of tweets for our comparison issue. These analyses provide an empirical demonstration of how the presented hybrid method can analyze defined communication sentiment and topics from large-scale social media data sets. The implications for communication scholars are discussed. [ABSTRACT FROM PUBLISHER]

Living in an age of big data, this study explores (a) how much certain online information is shared by media users and (b) what sentiments do social media users predominantly express on Twitter. Quantitative findings indicate that after the 2011 nuclear disaster at Fukushima Japan, the amount of nuclear energy–related tweets that were linked to outside information far outnumbered tweets containing no external link. Results also indicate that the predominant tone in these tweets was one of pessimism about nuclear energy. [ABSTRACT FROM AUTHOR]

Portrayals of female scientists in science fiction tend to be rare and often distorted. Our research investigates the social media discourse related to public perceptions of the portrayals of scientists in science fiction. We explore the following questions: How does audience discourse about a female scientist protagonist in a science fiction film compare with that about a male scientist in a comparable movie? And, what fraction of discourse in each case is dedicated to (a) comments on physical appearance and (b) incredulity that the character is a scientist? Using automated nonparametric sentiment analysis software that employs an intelligent algorithm informed by human coding, we analyze Twitter discourse around the release of two summer 2011 science fiction blockbusters with scientists in lead roles: Thor and Rise of the Planet of the Apes. Although scholars have pointed to sexualized portrayals of women scientists in popular media, we found relatively few mentions of the attractiveness of an admittedly attractive female scientist. Additionally, audience discourse was not centered on the implausibility of the female scientist. This is particularly meaningful when combined with previous research showing that strong women in lead roles do not decrease profitability of science fiction films. [ABSTRACT FROM AUTHOR]