The #MeToo Movement in the United States: Text Analysis of Early Twitter Conversations

Gray hair and pink slips: An analysis of Twitter responses to gendered ageism

When Canadian broadcaster, Lisa LaFlamme, announced in August 2022 that CTV National News did not renew her contract, some observers suggested that the corporation's decision resulted from LaFlamme's choice to "let her hair go gray" during the pandemic. An international public outcry ensued on Twitter. Our study involved an examination of these tweets (n = 440). Analyses revealed that approximately 80 percent of tweets indicated opposition to LaFlamme's dismissal, while only 2 percent indicated support and 18 percent indicated a neutral position. Among tweets expressing opposition, the most common justification, found in 79 percent of these tweets, centered on assessments of the employer's decision as poor. The frequency of all other justifications for opposition was considerably lower, with only 26 percent of these tweets mentioning ageism, 22 percent mentioning sexism, and 20 percent mentioning a general sense of unfairness to LaFlamme. These findings suggest the salience of capitalist logics in shaping how the public frames gendered ageism in the workplace. Our analyses also suggest a view of responses to this inequality as personal bodywork choices. Together, these framings reflect a more individual- than structural-level critique of gendered ageism, knowledge of which can inform efforts to dismantle it.

Cross-sectional examination of correlates of sexual victimization disclosure via #MeToo

Background: Sexual victimization is a serious public health problem, with a range of negative impacts on mental and physical health. Responses that individuals get to disclosure of sexual victimization play an important role in recovery. With the increased use of social media, more survivors are talking about their experiences of sexual victimization online. Research is needed to document the correlates of online disclosure of sexual victimization.Objective: The current study examined the role of demographic characteristics, assault severity, coping strategies, and social isolation as putative correlates of disclosing sexual victimization online via the hashtag #MeToo.Methods: A sample of 637 adults recruited via social media who reported a history of sexual victimization since the age of 14 completed self-report surveys using online survey software to assess disclosure of sexual victimization, assault severity, coping strategies, and social isolation.Results: Multivariate analyses suggest that levels of emotion-focused coping were positively associated with the disclosure of sexual victimization online via #MeToo. Further, individuals who had experienced completed rape by verbal coercion were less likely to disclose via #MeToo, compared to individuals who experienced other forms of assault.Conclusion: Coping strategies and assault severity play an important role in determining whether survivors disclose sexual victimization online via #MeToo. Findings suggest that individuals may disclose via #MeToo to seek support or express their emotions. Further, individuals whose sexual victimization experiences do not conform to 'typical' sexual victimization experiences are less likely to disclose via #MeToo.

Using Twitter-Based Data for Sexual Violence Research: Scoping Review

BACKGROUND

Scholars have used data from in-person interviews, administrative systems, and surveys for sexual violence research. Using Twitter as a data source for examining the nature of sexual violence is a relatively new and underexplored area of study.

OBJECTIVE

We aimed to perform a scoping review of the current literature on using Twitter data for researching sexual violence, elaborate on the validity of the methods, and discuss the implications and limitations of existing studies.

METHODS

We performed a literature search in the following 6 databases: APA PsycInfo (Ovid), Scopus, PubMed, International Bibliography of Social Sciences (ProQuest), Criminal Justice Abstracts (EBSCO), and Communications Abstracts (EBSCO), in April 2022. The initial search identified 3759 articles that were imported into Covidence. Seven independent reviewers screened these articles following 2 steps: (1) title and abstract screening, and (2) full-text screening. The inclusion criteria were as follows: (1) empirical research, (2) focus on sexual violence, (3) analysis of Twitter data (ie, tweets or Twitter metadata), and (4) text in English. Finally, we selected 121 articles that met the inclusion criteria and coded these articles.

RESULTS

We coded and presented the 121 articles using Twitter-based data for sexual violence research. About 70% (89/121, 73.6%) of the articles were published in peer-reviewed journals after 2018. The reviewed articles collectively analyzed about 79.6 million tweets. The primary approaches to using Twitter as a data source were content text analysis (112/121, 92.5%) and sentiment analysis (31/121, 25.6%). Hashtags (103/121, 85.1%) were the most prominent metadata feature, followed by tweet time and date, retweets, replies, URLs, and geotags. More than a third of the articles (51/121, 42.1%) used the application programming interface to collect Twitter data. Data analyses included qualitative thematic analysis, machine learning (eg, sentiment analysis, supervised machine learning, unsupervised machine learning, and social network analysis), and quantitative analysis. Only 10.7% (13/121) of the studies discussed ethical considerations.

CONCLUSIONS

We described the current state of using Twitter data for sexual violence research, developed a new taxonomy describing Twitter as a data source, and evaluated the methodologies. Research recommendations include the following: development of methods for data collection and analysis, in-depth discussions about ethical norms, exploration of specific aspects of sexual violence on Twitter, examination of tweets in multiple languages, and decontextualization of Twitter data. This review demonstrates the potential of using Twitter data in sexual violence research.

Online Disclosure of Sexual Victimization: A Systematic Review

We map the available scientific literature on how and why victims of sexual violence use digital platforms in the aftermath of victimization. Twenty-four empirical studies on sexual victimization and online disclosure were identified by systematically searching Web of Science and PsycINFO, checking reference lists, and consulting authors about relevant publications. The literature on online disclosure of sexual victimization does not yield a coherent picture. International literature pays limited attention to the various components of online disclosure like the characteristics of victims who disclosure online and the characteristics of the disclosure messages. Most studies focused on motivations for and reactions to online disclosure. Victims of sexual violence disclose sexual victimization online to seek support for clarification and validation, unburdening, documenting, seeking justice, informing others, or commercial goals (individual-oriented disclosure) and to provide support, educate, and as a form of activism (other-oriented disclosure). Responses to online disclosure are predominantly positive. Negative responses are rare. This review provides a comprehensive overview of multidisciplinary empirical information and displays knowledge gaps in victimological research. Future research should use robust quantitative and/or qualitative designs with substantial sample sizes, comparing victims who do disclose their sexual victimization online to victims who do not and comparing disclosure on different online platforms to increase generalizability. Potential for online support is identified, in which online disclosure can serve as a relatively safe alternative to off-line disclosure. This offers points of intervention for assistance and victim support in facilitating the use of the internet for support for victims of sexual violence.

Understanding Discussions of Sexual Assault in Young Women on a Peer Support Mental Health App: A Content Analysis

Trauma narratives may have been influenced by the Me Too movement, with thousands of individuals disclosing sexual violence stories online. Youth, the largest demographic of online users, may prefer the anonymity of the Internet to discuss experiences of sexual assault. Understanding the ways that young women, especially those experiencing mental health difficulties, discuss their experiences is important as they are at higher risk of revictimization and continued poor mental health. We searched for terms related to acts of sexual assault on a mental health peer-support app, TalkLife, and compared the number of posts during the initial wave of the Me Too movement (October 2017-March 2018) to the same time period in the previous year (October 2016-March 2017). We found a significant increase in posts related to sexual assault of 49.7% between the Pre and Post Me Too time periods (p < .001), controlling for a general increase in posts. A content analysis of 700 randomly selected posts found that a substantial number of young women used TalkLife to discuss their experiences of sexual assault, and these self-disclosures were mostly hopeless or depressing in tone. Additionally, neither the nature nor the number of self-disclosures varied across time points. The negative tone of the self-disclosures in the current study is worrying because the way women talk about their trauma can shape how they understand it, which could lead to negative self-appraisal and continued mental health difficulties. Online spaces have the potential to support young women and facilitate help-seeking, but we must be attentive to how they are used.

Barriers to Reporting Sexual Violence: A Qualitative Analysis of #WhyIDidntReport

The present study conducted thematic analyses of tweets including #WhyIDidntReport (N = 500) to examine barriers to reporting sexual victimization. Barriers to reporting were identified across individual, interpersonal, and sociocultural levels of the social ecology. Common barriers to reporting included labeling of the experience, age, fear, privacy concerns, self-blame, betrayal/shock, the relation/power of the perpetrator, negative reactions to disclosure, and the belief-or personal experience-that reporting would not result in justice and societal norms.

Negative Attitudes and Beliefs Toward the #MeToo Movement on Twitter

Given recent and growing societal movements focusing on sexual assault, such as the #MeToo Movement, it is imperative to understand current attitudes about sexual assault and these movements. The aim of this study was to examine negative attitudes and beliefs about sexual assault in the context of the #MeToo Movement by qualitatively analyzing social media posts (i.e., tweets) containing the hashtag "metoo" on Twitter, a popular social media platform. The initial sample consisted of 4,559 tweets that were publicly posted by Twitter users across a four-day timeframe and represented events and attitudes regarding both about an accuser and an accused. Data were cleaned and coded, and a remaining 508 tweets were included because they contained content that was categorized as negative in their attitudes or beliefs (either through attitudes of condemnation or ambivalence towards accusers and/or the #MeToo Movement). Results from a qualitative content analysis of tweets demonstrated that negative attitudes and beliefs about sexual assault within the context of the #MeToo Movement were conveyed through six main themes: (1) invalidating the accusations made as part of the #MeToo Movement; (2) insisting, and likely believing, that accusations made were false; (3) claiming that there were alternative motives for those accusations; (4) showing a concern for the harm that accusations may cause those accused; (5) exhibiting concerns about the effects of the Movement on male power, privilege, and status; and (6) questioning the integrity of the #MeToo Movement as a whole. These findings provide a deeper understanding of attitudes and beliefs about the #MeToo Movement, including novel beliefs not often addressed in the literature. Most importantly, these findings show that rape culture continues to persist despite current interventions and social movements. Results underscore the need for future interventions that aim to educate and improve societal attitudes towards sexual assault.

Social Media Activism and Convergence in Tweet Topics After the Initial #MeToo Movement for Two Distinct Groups of Twitter Users

Online social media movements are now common and support cultural discussions on difficult health and social topics. The #MeToo movement, focusing on the pervasiveness of sexual assault and harassment, has been one of the largest and most influential online movements. Our study examines topics of conversation on Twitter by supporters of the #MeToo movement and by Twitter users who were uninvolved in the movement to explore the extent to which tweet topics for these two groups converge over time. We identify and collect one year's worth of tweets for supporters of the #MeToo movement (N = 168 users; N = 105,538 tweets) and users not involved in the movement (N = 147 users; N = 112,301 tweets referred to as the Neutral Sample). We conduct topic frequency analysis and implement an unsupervised machine learning topic modeling algorithm, latent Dirichlet allocation, to explore topics of discussion on Twitter for these two groups of users before and after the initial #MeToo movement. Our results suggest that supporters of #MeToo discussed different topics compared to the Neutral Sample of Twitter users before #MeToo with some overlap on politics. The supporters were already discussing sexual assault and harassment issues six months before #MeToo, and discussion on this topic increased 13.7-fold in the six months after. For the Neutral Sample, sexual assault and harassment was not a key topic of discussion on Twitter before #MeToo, but there was some limited increase afterward. Results of bigram frequency analysis and topic modeling showed a clear increase in topic related to gender for the supporters of #MeToo but gave mixed results for the Neutral Sample comparison group. Our results suggest limited shifts in the conversation on Twitter for the Neutral Sample. Our methods and results have implications for measuring the extent to which online social media movements, like #MeToo, reach a broad audience.

Sexual Victimization Among Men: A Qualitative Analysis of the Twitter Hashtag #UsToo

The present study sought to characterize use of the hashtag #UsToo on Twitter to disclose or comment on men's experiences of sexual victimization. A sample of 281 original content, English-language tweets containing the hashtag were collected from Twitter over five consecutive weekdays. Thematic content analysis was conducted by a three-person coding team (full team consensus, achieving 100% agreement). Researchers categorized tweets as either a disclosure of victimization (N = 6) or a response to this hashtag (N = 275). When responding to the hashtag, users commented on the emotional impact of victimization, provided positive responses within the forum (i.e., advocacy, call to action, raising awareness, and prosocial reactions), and also engaged in negative responses within the forum (i.e., distracting attention away from the experiences of victims, egocentric responses which called attention to themselves or others, and otherwise harmful reactions). Despite the popularity of the #MeToo hashtag to disclose personal experiences of violence victimization, Twitter users were unlikely to utilize the hashtag #UsToo to disclose personal experiences of sexual victimization. Results highlight a divergence between online behavior in response to a call for men's disclosure of sexual victimization using the hashtag #UsToo versus online behavior in response to a call for women's disclosure of sexual victimization using the hashtag #MeToo.

Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement

The #MeToo movement on Twitter has drawn attention to the pervasive nature of sexual harassment and violence. While #MeToo has been praised for providing support for self-disclosures of harassment or violence and shifting societal response, it has also been criticized for exemplifying how women of color have been discounted for their historical contributions to and excluded from feminist movements. Through an analysis of over 600,000 tweets from over 256,000 unique users, we examine online #MeToo conversations across gender and racial/ethnic identities and the topics that each demographic emphasized. We found that tweets authored by white women were overrepresented in the movement compared to other demographics, aligning with criticism of unequal representation. We found that intersected identities contributed differing narratives to frame the movement, co-opted the movement to raise visibility in parallel ongoing movements, employed the same hashtags both critically and supportively, and revived and created new hashtags in response to pivotal moments. Notably, tweets authored by black women often expressed emotional support and were critical about differential treatment in the justice system and by police. In comparison, tweets authored by white women and men often highlighted sexual harassment and violence by public figures and weaved in more general political discussions. We discuss the implications of this work for digital activism research and design, including suggestions to raise visibility by those who were under-represented in this hashtag activism movement. Content warning: this article discusses issues of sexual harassment and violence.

Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes

BACKGROUND

Many social media studies have explored the ability of thematic structures, such as hashtags and subreddits, to identify information related to a wide variety of mental health disorders. However, studies and models trained on specific themed communities are often difficult to apply to different social media platforms and related outcomes. A deep learning framework using thematic structures from Reddit and Twitter can have distinct advantages for studying alcohol abuse, particularly among the youth in the United States.

OBJECTIVE

This study proposes a new deep learning pipeline that uses thematic structures to identify alcohol-related content across different platforms. We apply our method on Twitter to determine the association of the prevalence of alcohol-related tweets with alcohol-related outcomes reported from the National Institute of Alcoholism and Alcohol Abuse, Centers for Disease Control Behavioral Risk Factor Surveillance System, county health rankings, and the National Industry Classification System.

METHODS

The Bidirectional Encoder Representations From Transformers neural network learned to classify 1,302,524 Reddit posts as either alcohol-related or control subreddits. The trained model identified 24 alcohol-related hashtags from an unlabeled data set of 843,769 random tweets. Querying alcohol-related hashtags identified 25,558,846 alcohol-related tweets, including 790,544 location-specific (geotagged) tweets. We calculated the correlation between the prevalence of alcohol-related tweets and alcohol-related outcomes, controlling for confounding effects of age, sex, income, education, and self-reported race, as recorded by the 2013-2018 American Community Survey.

RESULTS

Significant associations were observed: between alcohol-hashtagged tweets and alcohol consumption (P=.01) and heavy drinking (P=.005) but not binge drinking (P=.37), self-reported at the metropolitan-micropolitan statistical area level; between alcohol-hashtagged tweets and self-reported excessive drinking behavior (P=.03) but not motor vehicle fatalities involving alcohol (P=.21); between alcohol-hashtagged tweets and the number of breweries (P<.001), wineries (P<.001), and beer, wine, and liquor stores (P<.001) but not drinking places (P=.23), per capita at the US county and county-equivalent level; and between alcohol-hashtagged tweets and all gallons of ethanol consumed (P<.001), as well as ethanol consumed from wine (P<.001) and liquor (P=.01) sources but not beer (P=.63), at the US state level.

CONCLUSIONS

Here, we present a novel natural language processing pipeline developed using Reddit's alcohol-related subreddits that identify highly specific alcohol-related Twitter hashtags. The prevalence of identified hashtags contains interpretable information about alcohol consumption at both coarse (eg, US state) and fine-grained (eg, metropolitan-micropolitan statistical area level and county) geographical designations. This approach can expand research and deep learning interventions on alcohol abuse and other behavioral health outcomes.

Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?

Twitter’s APIs are now the main data source for social media researchers. A large number of studies have utilized Twitter data for diverse research interests. Twitter users can share their precise real-time location, and Twitter APIs can provide this information as longitude and latitude. These geotagged Twitter data can help to study human activities and movements for different applications. Compared to the mostly small-scale data samples in different domains, such as social science, collecting geotagged data offers large samples. There is a fundamental question whether geotagged users can represent non-geotagged users. While some studies have investigated the question from different perspectives, they did not investigate profile information and the contents of tweets of geotagged and non-geotagged users. This empirical study addresses this limitation by applying text mining, statistical analysis, and machine learning techniques on Twitter data comprising more than 88,000 users and over 170 million tweets. Our findings show that there is a significant difference (p-value < 0.001) between geotagged and non-geotagged users based on 73% of the features obtained from the users’ profiles and tweets. The features can also help to distinguish between geotagged and non-geotagged users with around 80% accuracy. This research illustrates that geotagged users do not represent the Twitter population.

The Hidden Pandemic of Family Violence During COVID-19: Unsupervised Learning of Tweets

BACKGROUND

Family violence (including intimate partner violence/domestic violence, child abuse, and elder abuse) is a hidden pandemic happening alongside COVID-19. The rates of family violence are rising fast, and women and children are disproportionately affected and vulnerable during this time.

OBJECTIVE

This study aims to provide a large-scale analysis of public discourse on family violence and the COVID-19 pandemic on Twitter.

METHODS

We analyzed over 1 million tweets related to family violence and COVID-19 from April 12 to July 16, 2020. We used the machine learning approach Latent Dirichlet Allocation and identified salient themes, topics, and representative tweets.

RESULTS

We extracted 9 themes from 1,015,874 tweets on family violence and the COVID-19 pandemic: (1) increased vulnerability: COVID-19 and family violence (eg, rising rates, increases in hotline calls, homicide); (2) types of family violence (eg, child abuse, domestic violence, sexual abuse); (3) forms of family violence (eg, physical aggression, coercive control); (4) risk factors linked to family violence (eg, alcohol abuse, financial constraints, guns, quarantine); (5) victims of family violence (eg, the LGBTQ [lesbian, gay, bisexual, transgender, and queer or questioning] community, women, women of color, children); (6) social services for family violence (eg, hotlines, social workers, confidential services, shelters, funding); (7) law enforcement response (eg, 911 calls, police arrest, protective orders, abuse reports); (8) social movements and awareness (eg, support victims, raise awareness); and (9) domestic violence-related news (eg, Tara Reade, Melissa DeRosa).

CONCLUSIONS

This study overcomes limitations in the existing scholarship where data on the consequences of COVID-19 on family violence are lacking. We contribute to understanding family violence during the pandemic by providing surveillance via tweets. This is essential for identifying potentially useful policy programs that can offer targeted support for victims and survivors as we prepare for future outbreaks.

Partisan public health: how does political ideology influence support for COVID-19 related misinformation?

This study analyzes over 4000 tweets related to six misinformation topics about the COVID-19 pandemic: the use of hydroxychloroquine as treatment, the use of bleach as a preventative measure, Bill Gates intentionally causing the virus, the Chinese Communist Party intentionally causing the virus, and the Deep State causing the virus to ruin the economy and threaten President Trump's reelection chances. Across 5 of 6 topics (excluding bleach), conservatives dominate the discourse on Twitter. Conservatives are also more likely than their liberal peers to believe in and push conspiracy theories that the Chinese Communist Party, Bill Gates, and the Deep State are working in conjunction to infect the population and enact a surveillance state. Pandemic related misinformation has previously been associated with decreased adherence to public health recommendations and adverse health effects and evidence from the current pandemic indicates that adherence to public health recommendations is starkly partisan. This study suggests that the political and informational polarization further facilitated by social media platforms such as Twitter may have dire consequences for public health.