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Despite the increasing use of digital media data in communication research, a central challenge persists – retrieving data with maximal accuracy and coverage. Our investigation of keyword-based data collection practices in extant communication research reveals a one-step process, whereas our cross-disciplinary literature review suggests an iterative query expansion process guided by human knowledge and computer intelligence. Hence, we introduce the WordPPR method for keyword selection and text data retrieval, which entails four steps: 1) collecting an initial dataset using core/seed keyword(s); 2) constructing a word graph based on the dataset; 3) applying the Personalized PageRank (PPR) algorithm to rank words in proximity to the seed keyword(s) and selecting new keywords that optimize retrieval precision and recall; 4) repeating steps 1–3 to determine if additional data collection is needed. Without requiring corpus-wide sampling/analysis or extensive manual annotation, this method is well suited for data collection from large-scale digital media corpora. Our simulation studies demonstrate its robustness against parameter choice and its improvement upon other methods in suggesting additional keywords. Its application in Twitter data retrieval is also provided. By advancing a more systematic approach to text data retrieval, this study contributes to improving digital media data retrieval practices in communication research and beyond. [ABSTRACT FROM AUTHOR]

This study investigated portrayals of women in STEM on TikTok focusing on their self-presentation of identity and use of platform features to promote audience engagement. This quantitative content analysis examined TikTok posts (N = 400) from a 3-month sample of 100 TikTok accounts by individuals identified as women in STEM. Results for STEM-focused posts showed that these content creators provided positive portrayals of their work as women in STEM and frequent displays of their STEM identity, particularly displays of STEM competence and self-recognition. However, findings also indicated that this TikTok community displayed other social group identities less frequently and used relatively few TikTok platform features that would likely enhance audience engagement. Results suggest that positive portrayals of women in STEM on TikTok are helpful for challenging gender-STEM stereotypes, but the dearth of displays of social group identities highlights a need for more diverse women in STEM role models on TikTok. In addition, the infrequent use of popular platform features appears to be a missed opportunity for broader audience engagement. Implications for social media science influencers, science communicators, and informal STEM outreach professionals are discussed. [ABSTRACT FROM AUTHOR]

Centering on social media's public- and profit-oriented nature, this study theorizes how social media users are empowered and constrained when participating in platform governance through user-initiated expressions on platforms. The empirical analysis focuses on user responses before and after Elon Musk's official acquisition of Twitter, utilizing cluster analysis and topic modeling to examine the volume and content of related discourses among different Twitter user groups. Our results point to user constraint in platform governance. Although a diverse set of users, such as partisans, bots, and cryptocurrency enthusiasts, spoke up, they had diverging and sometimes conflicting objectives; and partisans dominated the conversations. There was an upsurge in user volume and activity level post-acquisition among liberal users, whose critical voices on platform governance might have bolstered platform business. Potential bots also increased in volume and amplified political topics. Our findings shed light on the challenges of user-driven platform governance, underscoring the complex interplay between platform users, economy, and governance. [ABSTRACT FROM AUTHOR]

Beyond a consciousness-raising hashtag, #MeToo has become a transnational movement, crossing the borders of many societies. However, outsized attention has been paid to the manifestations of #MeToo in the US and on Twitter when the reach of this movement was not restricted to a single country, language, or platform. Drawing from the concept of hybridization, we seek to understand how the uses of #MeToo are contextualized across cultures, languages, and social media platforms. By establishing a macroscopic computational approach, we examine the global diffusion of #MeToo as a hybrid communicative process across different language groups (English, Spanish, and Korean) and social media platforms (Twitter, Facebook, and Instagram). Through time-series analysis and comparative descriptions of language groups and platforms, we demonstrate how discourse flows, language characteristics, and actors differ across cultural and platform contexts and how public discourse of #MeToo was reappropriated and re-signified in different parts of the world to localize connective action. [ABSTRACT FROM AUTHOR]

Political campaigns often feature jarring revelations against candidates. This study examines how audiences come to understand major campaign events, the extent to which they shape evaluations of candidates, and how their impact is filtered through an increasingly partisan news media environment. Using national rolling cross-sectional survey data collected over the 2016 U.S. presidential election period, we show partisan asymmetries in the way major campaign events influenced candidate appraisals. Event effects during the 2016 campaign were dependent on various media use patterns and concentrated among Independents. In particular, the reopening of the investigation into Clinton's email server by James Comey reduced her favorability, especially when paired with liberal and conservative partisan media use. By providing a nuanced picture of partisan selective exposure and campaign effects, our findings reinforce that the role of campaigns in candidate appraisals should be understood at the intersection of media use, partisanship, and specific events during a contentious race. [ABSTRACT FROM AUTHOR]

We examine how individuals' interactions with the shifting contemporary communication ecology—either by seeking information selectively from partisan sources or immersing themselves in a broad range of partisan communications — relate to shifting levels of social trust and online engagement. Using national panel surveys of young adults (i.e. millennials age 18–34) collected over the 2016 U.S. presidential election, we find that individuals' partisan communication flows—calculated by algorithmically combining patterns of news consumption, social media use, and political talk—explain: (a) polarized shifts in levels of trust towards people of other nationalities, religions, races, and ethnicities and (b) increases in levels of online political engagement. By elaborating the relationship between citizens' communication patterns and their levels of trust and participation, this research forces a reconsideration of theoretical traditions in the field of communication, especially those linking mass and interpersonal processes in the study of social capital. [ABSTRACT FROM AUTHOR]

Garnering coverage across the political spectrum is a major challenge for burgeoning social movements. The #MeToo movement stands out due to the volume of attention it generated. Yet, it is unclear how news media across the partisan spectrum covered the movement using different sexual violence language markers, latent topic, and word choices and which accusations and events drove media attention. To examine this, we used Media Cloud to extract 17,877 news articles from nine media outlets across the political spectrum, containing specific n-grams or co-occurrences of (1) "metoo," (2) "sexual misconduct," (3) "sexual harassment," and (4) "sexual assault" from October 2017 through February 2018. The analyses first examined whether language and attention differed across the ideological news ecology and then turned to time-series modeling of these discourses to examine what drove press coverage and structural topic modeling (STM) and term frequency-inverse document frequency (TF-IDF) analysis to understand latent topics and language usage. Findings reveal that (1) left-leaning media dedicated more relative attention across all topics—#MeToo, sexual misconduct, sexual harassment, and sexual assault—relative to centrist and right-leaning media. Moreover, across the right, left, and centrist media, the language markers "misconduct," "harassment," and "assault" decreased over the study period, while the mentions of #MeToo movement increased during the same period; (2) stories relating to entertainment and those accusing politicians, especially those belonging to the party in power at the Federal level, seemed to be by far the strongest driver of news media attention; and (3) we further observed partisan differences in topics of news coverage and language usage. [ABSTRACT FROM AUTHOR]

How did efforts that prompted the sharing of personal experiences of sexual violence and harassment around #MeToo coalesce into calls for action across a range of institutions and communities? We argue that sharing experiences of trauma in digital spaces created a network of acknowledgment, which supported and sustained nascent #MeToo activism based on the logic of connective action. This article attempts to (a) understand the temporal dynamics of these different discourses within the #MeToo movement on Twitter, (b) reveal the accounts animating these discourses and the most prominent themes within them, and (c) model the overtime relationship between these discourses and their relationship to major news event and #MeToo revelations. To do so, we analyze a 1% sample of tweets from the 5-month period following the revelations about Harvey Weinstein in early October 2017, employing a range of computational approaches, including part-of-speech tagging, dependency analysis, hashtags extraction, and retweet network analysis—to identify key discourses, actors, and themes. We then conduct time series analysis to identify the relationship between the two discourses and predict how the ebbs and flows of each discourse are shaped by news events. [ABSTRACT FROM AUTHOR]

The Russian-sponsored Internet Research Agency's (IRA) use of social media to influence U.S. political discourse is undoubtedly troubling. However, scholarly attention has focused on social media, overlooking the role that news media within the country played in amplifying false, foreign messages. In this article, we examine articles in the U.S. news media system that quoted IRA tweets through the lens of changing journalism practices in the hybrid media system, focusing specifically on news gatekeepers' use of tweets as vox populi. We find that a majority of the IRA tweets embedded in the news were vox populi. That is, IRA tweets were quoted (1) for their opinion, (2) as coming from everyday Twitter users, and (3) with a collection of other tweets holistically representing public sentiment. These findings raise concerns about how modern gatekeeping practices, transformed due to the hybrid media system, may also unintentionally let in unwanted disinformation from malicious actors. [ABSTRACT FROM AUTHOR]