“I Definitely Did Not Report It When I Was Raped . . . #WeBelieveChristine #MeToo”: A Content Analysis of Disclosures of Sexual Assault on Twitter

Abstract

The “Me Too” movement, founded by activist Tarana Burke, began in 2006, before Twitter became the viral platform for political activism and news media that it is known for today. The reemergence of the Me Too movement on Twitter in 2017 sparked a widespread focus on the societal issue of sexual misconduct. This study examines sexual assault disclosures as an aspect of such misconduct through the context of the Me Too movement on Twitter. Through the use of content analysis, online disclosures of sexual assault (N = 1,459) are examined for variations of sexual explicitness and attainment of social functions per the functional theory of self-disclosure. Specifically, this study explores associations between Twitter network structure and (1) levels of sexual explicitness and (2) retweet count. Through manual coding and statistical analysis, the study finds associations between sexual explicitness of online disclosures and retweet count as well as associations between network structure and sexual explicitness of disclosures. The study shifts the focus of disclosure discourse from dyadic communication to the contemporary context of networked social media. Implications for theory and practice are discussed, which include, but are not limited to, the exploration of associations between disclosing and catharsis, disclosing that may be characterized as neutral or conflicted, and disclosing sexual assault without being sexually explicit.

Keywords

sexual assault Me Too #MeToo Twitter self-disclosure sexual explicitness

The “Me Too” movement was founded in 2006 by social activist Tarana Burke (Xiong et al., 2019), who sought to bring awareness to the issue of sexual assault experienced by women of color (Burke, 2017). For over a decade, Burke has been tirelessly advocating, writing, and speaking to bring attention to the broader issue of sexual assault and, more specifically, the issues of sexual violence and sexual harassment against girls and women of color. Over a decade after Burke founded the Me Too movement, it was popularized in social media by a post from Alyssa Milano in 2017 (Kearl, 2018) after news broke of the sexual misconduct allegations against Harvey Weinstein in October 2017. Those events marked an inflection point in the prevalence of sexual harassment, sexual assault, and sexual victimization topics in social media and contributed significantly to the widespread #MeToo movement (Starkey et al., 2019). Intending to shed light on how frequently behaviors of sexual misconduct occur, the Me Too movement includes disclosure narratives by survivors as well as responses to survivor disclosures (Tolentino, 2018). The #MeToo hashtag invites survivors to share their experiences, but beyond digital platforms, the movement itself encompasses more: It advocates for awareness of the prevalence of sexual misconduct throughout society and the (detrimental) impacts such experiences may have on survivors, their social networks, and organizations. Furthermore, the reemergence of Me Too and its uprising on Twitter led to the development of other movements dedicated to raising awareness of sexual misconduct, including Time’s Up and #YESALLWOMEN. As a result, the #MeToo movement has begun to receive attention from scholars (e.g., Battaglia et al., 2019). With the uprising of #MeToo came a surge of interdisciplinary research on sexual assault, disclosures, and the Me Too movement (Bogen, Millman, et al., 2018; Bogen et al., 2019; Chowdhury et al., 2019; Evans, 2018). To date, however, such research has not included studies that focus specifically on the behavior of disclosing from a perspective of analyzing big data trends.

Although self-disclosure, in general, and disclosure of traumatizing experiences, in particular, have been examined from a number of theoretical perspectives, few of these approaches have been applied to online fora or modalities. Studies that examined self-disclosure in online media generally have examined relatively routine forms of disclosure (e.g., Desjarlais et al., 2015; Dworkin et al., 2016; Hollenbaugh & Ferris, 2015; Utz, 2015; Varnali & Toker, 2015; Xie & Kang, 2015). Research examining sexual assault disclosures have typically focused on the process in face-to-face or recalled forms (e.g., Bicanic et al., 2015; Campbell et al., 2015; Lorenz et al., 2018). Others have focused on the motives (e.g., Hollenbaugh & Ferris, 2014) or social reactions and effects (e.g., Jackson et al., 2017; Kirkner et al., 2018; Paul et al., 2014) to such online disclosures of sexual assault.

The generalizability of traditional theories of self-disclosure to online media remains an open question. For example, previous research has explored the application of the functional theory of self-disclosure to Facebook (Bazarova & Choi, 2014). Specifically, Bazarova and Choi (2014) investigated disclosure from a functional perspective in public and private Facebook posts (Bazarova & Choi, 2014; Choi & Bazarova, 2015). While some of the processes and functions of sexual assault narratives (Bletzer & Koss, 2004; Cairns, 1994; Muldoon et al., 2016) and disclosures (Nguyen et al., 2012) in online media are likely to traverse offline and online modalities, research indicates that digital fora have resulted in a digital narrative form with unique vernacular. “These new digitized narratives not only shape what is disclosed and known about sexual violence, but what is felt and experienced” (Mendes et al., 2019, p. 1304). The aim of this research has been to apply the functionality of self-disclosure to the specific context of sexual assault–related content on the social networking site (SNS) Twitter, specifically through a content analysis of disclosure tweets.

The current study analyzes the #MeToo movement on Twitter because of the relevance of the platform to the movement. The concept of “me too” as it relates to sexual misconduct dates back to 2006 on Myspace (Rodino-Colocino, 2018), but its virality on Twitter as of October 2017 has elicited and evoked extensive attention on the platform, thereby propelling the movement into mainstream media. As of its 1-year anniversary, #MeToo had been used approximately 19 million times (Pew Research Center, 2018). Currently, 38% of young adults (ages 18–29 years) are active on Twitter (Aslam, 2020), indicating that the platform’s relevance amid a society driven by social media is worth examining. Many communicative behaviors are enacted online, which is why it is imperative that behavior on social media platforms, particularly as it relates to societal problems such as sexual misconduct, should be integrated into theoretical frameworks and future communication research.

Self-disclosure broadly “includes any information exchange that refers to the self, including personal states, dispositions, events in the past, and plans for the future” (Derlega & Grzelak, 1979, p. 152). Original functional conceptions of self-disclosure included the parameters of breadth and depth (Altman & Taylor, 1973; Cozby, 1973), and other scholars have noted the multidimensionality of the concept, which includes the components of honesty, intent, and the positive or negative valence of the information to the discloser (Wheeless & Grotz, 1976). Whereas the functions of sexual assault disclosure have been studied in face-to-face contexts, there is a need to investigate the process in online contexts such as Twitter. To conceptualize disclosure in this modality, it is important to examine what is currently known about the process of sexual assault disclosures in general.

Disclosures of Sexual Assault

A prevalent theme among disclosure discourse as it relates to sexual assault is the role of social support as a communicative tactic (Bogen, Leach, et al., 2018; DeLoveh & Cattaneo, 2017; Demers et al., 2017). In particular, observed social support has been linked with recipient reactions (e.g., Ullman, 1996). Advancements in technology have made it possible to expand upon studies such as Ullman’s (1996), with the ability to communicate digitally with significantly larger audiences (Fawcett & Shrestha, 2016). With social media serving as modes of communication for 88% of adults (Smith & Anderson, 2018), their role in the discourse of sexual assault disclosures must not be neglected.

Research on sexual assault disclosures has been continued through further examinations of social support sought by victims (Ullman, 1999) as well as demographic and assault factors related to social support (Ullman & Filipas, 2001). Even when research on disclosures incorporated factors of perceived control and posttraumatic stress disorder (PTSD) symptoms (Ullman & Peter-Hagene, 2014), the scope of disclosure discourse relating to sexual assault did not extend beyond interpersonal communication research. If anything, the continued examinations of sexual assault disclosures shifted focus to the reactions of disclosure recipients (Branch & Richards, 2013; Orchowski & Gidycz, 2012) and the communication challenges faced by being a confidant for a disclosing survivor (Middleton et al., 2016; Milliken et al., 2016). As such, the role of social media in the social construction of sexual assault victimization and support has yet to be seriously investigated.

#MeToo and the Topic of Sexual Misconduct on Social Media

The #MeToo movement has emerged as a topic of interest in the media and in fields such as medicine (Jagsi, 2018) and women’s studies (Mendes et al., 2018); however, its presence within communication research has not yet received equal observation. A few qualitative analyses have examined rape culture themes (Stubbs-Richardson et al., 2018), themes of online disclosures of sexual assault (Bogen et al., 2018), and the online social reactions to sexual assault disclosures on Twitter (Bogen et al., 2019). Considering the limitations of the chosen method for the previous studies, quantitative research focusing on sexual assault disclosures limits personal biases from warping interpretations of data, which have been acknowledged as problematic (Bogen et al., 2018). Furthermore, Twitter users may have shared exaggerated, untruthful, or mocking disclosures upon being prompted by a public figure to use a specific hashtag in participating in a movement (Bogen et al., 2018). The disclosure tweets that comprised the data may not have been genuine but rather were possibly shared by Twitter users who provided information out of a desire to belong. This study is uniquely positioned to examine online disclosures of sexual assault without the potential for prompts or biases to hinder the data and subsequent analyses.

#MeToo operates as a clickable feature on Twitter and, since its uprising 2017, has transformed into a widespread social media movement. Its potential functionality, however, extends beyond Twitter. Its association with narratives of disclosures of rape and sexual assault has opened opportunities for people to disclose their experiences, even if they do not tweet using the hashtag or the phrase without the “#.” For the purposes of this study, #MeToo has served as a starting point for how disclosures on Twitter by survivors of sexual assault will be content analyzed while also including associated relevant hashtags such as #WhyIDidntReport and #IBelieveHer.

Theoretical Underpinnings: The Functional Theory of Self-Disclosure

To pursue an extension of the discourse of sexual misconduct disclosures, this study utilizes the functional theory of self-disclosure (Derlega & Grzelak, 1979; Prager et al., 1989; L. B. Rosenfeld & Kendrick, 1984). The theory asserts that there are five classes of functionality of a self-disclosure. These classes include (1) expression, (2) self-clarification, (3) social validation, (4) relationship development, and (5) social control. Expression is defined as the cathartic function of a self-disclosure. An opportunity to disclose provides an individual with a way to release feelings or emotions that have been repressed (Derlega & Grzelak, 1979). Self-clarification serves a cognitive function of a self-disclosure. When individuals self-clarify during a disclosure, they are clarifying their own opinions or issues regarding the issue at the center of the disclosure (Derlega & Grzelak, 1979). Social validation involves seeking feedback from an audience, which ultimately can validate the discloser’s self-concept (Derlega & Grzelak, 1979). Relationship development is defined as a reward function of a self-disclosure in which the disclosure facilitates the development and maintenance of a relationship with the message receiver(s) because of the perceived rewarding nature of information exchange (Derlega & Grzelak, 1979). Last, the function of self-control in a self-disclosure is oriented to the personal information of the discloser. Through selectivity of personal details and target of disclosure, self-disclosure functions to maintain control of the communication and the social relationship (Derlega & Grzelak, 1979; L. B. Rosenfeld, 1979).

Until recently, the five functions of disclosure have been conceptualized as social affordances in the interpersonal context; however, more research on applications of disclosure functions and social media is needed (e.g., Karahanna et al., 2018; Rathnayake & Winter, 2018). In a similar way to how Bazarova and Choi (2014) applied functionality of self-disclosure to Facebook statuses, this study seeks to incorporate components of the theory to the online setting, specifically Twitter, thus prompting the first research question:

RQ₁: What are the most common types of social affordances sought in disclosing experiences of sexual assault on Twitter?

Much of the research on disclosures, including disclosures of sexual assault, has focused on the human subjects who enact the communication (Ahrens & Aldana, 2012; Ahrens et al., 2010). In this way, other modes of message-sending and receiving, such as interaction in social media, have been neglected. Granted, there have been examinations of disclosures on social media platforms (e.g., Clark-Gordon et al., 2019; Leighton et al., 2018; Nguyen et al., 2012; Ruppel et al., 2017). For example, disclosures of depression on Instagram (Andalibi et al., 2017) have received scholarly attention, as have the relationships between disclosure and intimacy among social media platforms (Bazarova, 2012; Jiang et al., 2011; Ma et al., 2016). It is not at all clear, however, that such research generalizes to disclosures of sexual assault or the #MeToo movement on Twitter, given the potential political, organizational, and interpersonal and social support motives that may be involved.

Breadth, Depth, and Explicitness as They Relate to Theoretical Underpinnings

Revelations of personal information may vary at least in their degrees of explicitness as well as breadth and depth (Altman & Taylor, 1973; Cozby, 1973; Derlega & Grzelak, 1979; Wheeless & Grotz, 1976). While some individuals may disclose personal information at high degrees of breadth, depth, and explicitness, such as the status of their mental health or their sexual orientation (Bazarova & Choi, 2014), others may only be willing to reveal general information, such as how they are feeling at a particular moment (Derlega & Grzelak, 1979).

People also use social media as means by which they record self-disclosures (Bazarova, 2012; Bazarova & Choi, 2014; Choi & Bazarova, 2015; Jiang et al., 2011). Twitter is one of the most frequented and actively used social media platforms (Smith & Anderson, 2018), but not every user engages with the same networks. While sexual explicitness has received attention from researchers (Bleakley et al., 2012; Leone, 2002), there appears to be no research specifically on sexual assault disclosure explicitness. Lin et al. (2014) found that individuals with larger networks on Facebook have a stronger need for impression management with regard to the content they post. For this study, impression management may be an influential factor in the relationship between Twitter network size and the level of explicitness of a disclosure. The details of a sexual assault that are part of explicit disclosures may be included as a strategy for impression management; for instance, such disclosures may be designed in an effort to avoid victim blaming and victim shaming. Because sexual assault has garnered rapid widespread attention in the last year, people may want to be involved with the conversation, but in such a way that does not reflect poorly on them, particularly because the topic is so public. In addition, Xie and Kang (2015) found that SNS users disclose more information when their network sizes are larger. They also found that when users’ networks are comprised of strangers or people they have never met in person, their willingness to self-disclose online increases. While neither study focuses on Twitter network size and the association with explicitness of a disclosure, their findings are suggestive that network size may play a role in how people self-disclose online. As such,

H₁: The size of a user’s network will be positively associated with the level of sexual explicitness of their online disclosure.

From its start in March 2006, Twitter content was limited to 140 characters, which included spaces and punctuation marks. November 7, 2017, marked the platform’s official update from 140 characters per tweet to 280 characters per tweet (Tsukayama, 2017). This update, although still limiting how much content any user may include per individual post, has had effects on the amount and type of content that comprise tweets (Gligoric et al., 2018). Twitter also offers the retweet feature, which allows users to repeat someone else’s content to their own profile and public feed without having to retype it. Tweets that are made up of relatable sentiments, keywords, and expressions of interest receive more retweets than tweets that do not include these features (Vougioukas et al., 2017). Furthermore, as evidenced by Tsugawa and Ohsaki (2015), negativity bias is highly influential in social media because it asserts that negative things have a stronger impact on people than do positive things (Baumeister & Bratslavsky, 2001; Rozin & Royzman, 2001; Taylor, 1991). This would explain why negative messages spread more rapidly on social media, as well as Tsugawa and Ohsaki’s (2015) finding that negative tweets receive larger volumes of retweets than do neutral and positive tweets. Given that victims of sexual assault are likely to speak of their experiences in negative terms (Holland & Cortina, 2017; Katz et al., 2017; Starzynski et al., 2017), sexual explicitness in tweet content of online disclosures is expected to be associated with the number of times a post is retweeted:

H₂: Level of explicitness of an online disclosure will be positively associated with the number of times it is retweeted.

Finally, while Twitter limits its users in terms of how many characters they may use per tweet, it does not censor the content that comprises each tweet. Twitter users are free to include anyone’s name in their tweets and tag specific users with the use of “@” before a user’s handle. With Twitter’s lack of prohibition regarding explicitness of content and inclusion of people’s names, the final research question is posed:

RQ₂: Do online disclosures that reveal the perpetrator’s identity receive more retweets?

Method

Sample

Data from Twitter users (tweets) living in eight major U.S. cities were analyzed for the purposes of this study (Boston, Chicago, El Paso, Los Angeles [LA], Miami, New York [NY], Philadelphia, and Phoenix). These cities were selected for analysis following research of the violent crime rates of the United States. Each of these eight cities holds high recorded numbers of reported rapes to law enforcement (Federal Bureau of Investigation: Uniform Crime Reporting [FBI: UCR], 2016a, 2016b, 2016c, 2016d, 2016e, 2016f, 2016g, 2016h). Regarding violent crime reporting, rape is the most underreported crime in the United States. An estimated 63% of sexual assaults are not reported to law enforcement (Rennison, 2002); as such, it is critical to consider that the reported numbers of rape by the FBI: UCR reports are lower than the actual number of rapes and sexual assaults that occur in each city.

Data Collection

A collection of tweets about disclosures of sexual assault was obtained through the use of the Social Media Analytic and Research Testbed (SMART) dashboard (Tsou et al., 2015; Yang et al., 2016). Through an integration of a social media application program interface (API), keywords and phrases within the data were observed. The topic of disclosures of sexual assault were defined by specific key terms. These key terms were entered into the SMART dashboard; then, a Twitter search API collected and filtered data on a daily basis. This method only collected new data as they came through the API filter; no disclosures on Twitter prior to August 2018 were collected as part of this study. Once collected, data were stored and accessible on a dashboard through the MongoDB database (Tsou et al., 2017), which displayed visuals of the collected data.

The key terms for this study were determined through combinations of specific words that relate to the general theme of sexual misconduct and to the more specific themes of rape, sexual assault, and sexual harassment. Several key terms contain hashtagged (the use of “#” in front of a word or phrase in a tweet, turning the word or phrase into a link that directs users to all other associated tweets) words or phrases. The hashtags represent the trending Me Too movement that has maintained public attention since its uprising on Twitter in 2017. These terms comprise part of the list because of the movement’s direct relevance to the broad theme of sexual misconduct. The key terms that were entered into the SMART dashboard were the following: I was raped, I was raped + consent, I was raped + #metoo, I was raped + me too, I was sexually abused + #metoo, I was sexually abused + me too, I was sexually harassed + #metoo, I was sexually harassed + me too, I said no + consent + sex, and I survived + rape. The list of key terms entered into the SMART dashboard for this study is not indicative of all terms associated with themes of sexual misconduct. Certain words, including victim and survivor, as well as other combinations of these terms, were not included on the list entered into the SMART dashboard due to their binary nature. These terms were still gathered as part of the data collection if they were combined with any of the words or phrases on the determined list.

A total of 41,671 tweets were collected and stored between August and November 2018. A total of 40,087 nonunique tweets (retweets) comprised the total collection. A random sample of 1,458 tweets from unique users in seven of the eight cities was drawn using a random number generator. The Twitter API collected no tweets from El Paso; therefore, the city did not contribute to the study. Tweets from LA and NY contributed most of the data (LA = 25.6%; NY = 39.3%). The sample comprised original tweets (71.1%) and retweets (28.9%), with each retweet only appearing once in the data set to avoid redundancies in repeated content.

Coding Procedure

To obtain the data for this content analysis, a codebook was developed based on the model provided by Martinez et al. (2018). Table 1 provides the full codebook used for this content analysis. Given no prior category systems for online sexual assault disclosure, code definitions and categories were developed using Altman and Taylor’s (1973) and Cozby’s (1973) self-disclosure parameters of breadth and depth as well as the social affordances components of Derlega and Grzelak’s (1979) functional theory of self-disclosure. Features of the Twitter platform also contributed to the development of code definitions and categories.

Table 1.

Content Analysis Codebook.

Category	Coded theme	Code	Definition	Gwet’s agreement coefficient
User	Network size		The Twitter profile indicates the number of followers of the user.	.99
Tweet	Number of retweets		The tweet indicates how many times the user has been retweeted by other users.	.99
Tweet	Number of tweets by user		The Twitter disclosure indicates that more than one tweet comprises the entire disclosure (e.g., “1/5, 2/5, 3/5, etc.”).	.99
Tweet	Name mention	No = 0Yes = 1	The Twitter disclosure includes the name of the perpetrator.	.96
Disclosure	Breadth	No = 0Yes = 1	The Twitter disclosure is not solely focused on the disclosure of sexual assault.	.94
Disclosure	Depth	No = 0Yes = 1	The information that comprises the Twitter disclosure is personal information of the user.	.95
Disclosure—Filter	Is it a self-disclosure?	No = 0Yes = 1	The tweet is a self-disclosure if it is coded: breadth = 0, depth = 1.Tweets that receive a no = 0 code in the disclosure (filter) category will receive a 0 for each variable thereafter.Tweets may only be coded as yes = 1 if they are original content of the user; retweets and quoted material do not qualify as self-disclosures.	.95
Linguistic	Sexual explicitness	Not sexually explicit = 0Somewhat sexually explicit = 1Very sexually explicit = 2Extremely sexually explicit = 3	Not sexually explicit disclosure tweets do not refer to any of the behaviors listed below.Somewhat sexually explicit disclosure tweets refer to unwanted and nonconsensual kissing on the lips and unwanted and nonconsensual removal of clothing.Very sexually explicit disclosure tweets refer to unwanted and nonconsensual fondling behaviors (e.g., groping, molestation, abusing, assaulting) while clothed or partially clothed.Extremely sexually explicit disclosure tweets refer to unwanted and nonconsensual foreplay behaviors while naked, unwanted and nonconsensual intercourse, or other unwanted and nonconsensual sexual penetration or masturbation.	.89
Affordance	Expression	No = 0Yes = 1	The disclosure within the tweet serves a cathartic function; the discloser expresses his or her feelings or emotions related to the experience of the sexual assault.	.86
Affordance	Self-clarification	No = 0Yes = 1	The disclosure within the tweet serves a function of cognitive processing; the disclosure clarifies the discloser’s own thoughts or issues regarding the experience of the sexual assault.	.85
Affordance	Social validation	No = 0Yes = 1	The disclosure within the tweet is included to obtain feedback and validate the discloser’s self-concept.	.85
Affordance	Relationship development	No = 0Yes = 1	The disclosure within the tweet includes information about the discloser’s desire to stand in solidarity with other victims of sexual assault.	.92
Affordance	Social control	No = 0Yes = 1	The disclosure within the tweet includes specific information about the discloser. The discloser may share information he or she had kept private for a long time, or the disclosure may include personal characteristics or demographic information.	.84

Tweets were coded for social affordances only if they were determined to be self-disclosures. To determine whether a tweet was a self-disclosure of an experience of sexual assault, each entry was coded for the parameters of breadth and depth. Breadth refers to the amount of information disclosed while depth refers to the intimacy of the information disclosed (Altman & Taylor, 1973; Cozby, 1973). For the purposes of this study, breadth was determined by the definition, “The Twitter disclosure is not solely focused on the disclosure of the sexual assault,” and was coded as 0 = no or 1 = yes. Depth was determined based on whether the information that comprised the Twitter disclosure was personal information of the user, and was coded as 0 = no or 1 = yes. In order for a tweet to be considered a self-disclosure, it had to be coded as breadth = 0, depth = 1. This specific coding is mandated by the definition of self-disclosure, which claims that “self-disclosure includes any information exchange that refers to the self, including personal states, dispositions, events in the past, and plans for the future” (Derlega & Grzelak, 1979, p. 152). This combination of coding ensured that the content of the tweet focused specifically on the topic of sexual assault (breadth) and represented personal information being revealed by the discloser (depth), thus constituting a self-disclosure.

Upon being coded as a self-disclosure (0 = no, 1 = yes), each tweet was then coded for sexual explicitness. The social affordances of functional self-disclosure presented by Derlega and Grzelak (1979) were then coded according to the definition of each affordance and their existence in a tweet. Specifically, the affordance of expression was defined as, “The disclosure within the tweet serves a cathartic function; the discloser expresses his or her feelings or emotions related to the experience of sexual assault,” and was coded as 0 = no or 1 = yes, depending on whether the disclosure tweet comprised content that matches the definition. Self-clarification was defined as, “The disclosure within the tweet serves a function of cognitive processing; the disclosure clarifies the discloser’s own thoughts or issues regarding the experience of sexual assault,” and was coded as 0 = no or 1 = yes. To distinguish self-clarification, from expression, self-clarification was operationalized as the cognitive function of a disclosure and expression was operationalized as the affective function. Specifically, when coding for the affordance of self-clarification, words were specifically examined that conveyed thoughts (e.g., “I think/thought . . ., I knew/didn’t know, etc.”), whereas the process of coding for expression included looking for words that conveyed feelings (e.g., “I feel/felt [emotion] . . ., I was afraid/sad/angry/etc.”). Social validation was defined as, “The disclosure within the tweet is included to obtain feedback and validate the discloser’s self-concept,” and was coded as 0 = no or 1 = yes. Relationship development was defined as, “The disclosure within the tweet includes information about the discloser’s desire to stand in solidarity with other victims of sexual assault,” and was coded as 0 = no or 1 = yes. Finally, social control was defined as, “The disclosure within the tweet includes specific information about the discloser; the discloser may share information he or she had kept private for a long time, or they may include personal characteristics or demographic information,” and was coded as 0 = no or 1 = yes.

While examinations of sexual explicitness receive attention from present scholars (Dillman Carpentier et al., 2017; Vangeel et al., 2020; Wright & Tokunaga, 2015), the existing literature has not included a measure designed to capture explicitness of sexual assault disclosures on Twitter. The development of a coding scheme for sexual explicitness was guided by other research, in particular, studies examining sex content in the media (Leone, 2002) and men’s and women’s portrayals in media entertainment (Bleakley et al., 2012). These studies used measures categorizing sexually explicit content on a continuum, ranging from 0 = no sexually explicit content to 1 = somewhat sexually explicit content in the form of kissing on the lips and removal of clothing, to 2 = very sexually explicit content, including fondling and groping behaviors while clothed or partially clothed, and 3 = extremely sexually explicit content such as foreplay behaviors while naked, intercourse, or other sexual penetration or masturbation. For this study, a measure based on this prior research was adapted to be more specific to sexual assault, for the purposes of assessing explicitness of sexual assault disclosures on Twitter. Specific changes that were made to better adapt the prior measures to the topic of sexual assault included using the terms unwanted and nonconsensual to describe the sexual behaviors.

Analytical Procedure

The codebook developed for this study classified measures by characteristics and information content of tweets. Characteristics of the sample were analyzed from publicly available information on each Twitter profile. These qualitative characteristics included network size of each Twitter user and the number of tweets that comprised each single disclosure of sexual assault. Tweet content of sexual assault disclosures served as the broad theme of the codebook, from which more specific categories were established to provide more specific information about each tweet.

The category under the Linguistic characteristic was Explicitness. To analyze explicitness, variations of sexually explicit content within the context of online sexual assault disclosures were examined to determine whether sexual explicitness was included in a tweet. For this study, sexually explicit content ranged from not sexually explicit (e.g., 0 = not explicit disclosure tweets do not refer to any of the behaviors listed below) to somewhat sexually explicit (e.g., 1 = somewhat explicit disclosure tweets refer to unwanted and nonconsensual kissing on the lips and unwanted and nonconsensual removal of clothing), to very sexually explicit (e.g., 2 = very explicit disclosure tweets refer to unwanted and nonconsensual fondling behaviors [e.g., groping, molestation, abusing, assaulting] while clothed or partially clothed), to extremely sexually explicit (e.g., 3 = extremely explicit disclosure tweets refer to unwanted and nonconsensual foreplay behaviors while naked, unwanted and nonconsensual intercourse, or other unwanted and nonconsensual sexual penetration or masturbation).

The coded themes that followed Explicitness were analyzed under Disclosure. These themes included expression, self-clarification, social validation, relationship development, and social control. Each of the themes within the Disclosure category represents one of the five social affordances of Derlega and Grzelak’s (1979) functional theory of self-disclosure.

To ensure reliability, two coders randomly coded a subset of 210 of the collected tweets over three rounds. Upon completion of the independent coding of each round, the coders discussed any disagreements. After an acceptable level of reliability was achieved, the collected tweets that were not part of the reliability analysis were single-coded per codebook classifications and definitions. We used Gwet’s agreement coefficient (Gwet, 2002) to determine intercoder reliability as it compensates for limitations of other coder reliability statistics (Wongpakaran et al., 2013), including as Cohen’s Kappa and Scott’s Pi, both of which are sensitive to prevalence (Parker et al., 2013). Given that variables in our study (such as affordances related to social validation and relationship development) demonstrated fairly low prevalence in the data, we relied on Gwet’s agreement coefficient. After an acceptable level of reliability was achieved (average Gwet’s agreement coefficients across all coding = .93, ranging from .84 to .99), the collected tweets that were not part of the reliability analysis were single-coded per codebook classifications and definitions (see Table 1 for details on reliability for individual variables).

Results

Descriptive Statistics

Table 2 presents distributions of the variables of the five social affordances of functional self-disclosure, along with descriptive statistics of primary variables: network size (Mdn = 536, Mode = 5), retweet count (M = 76.1, SD = 1,633.6), and sexual explicitness (M = 1.4, SD = 1.49). Only a very small portion of tweets accused someone by name (2.7%). The associations between the primary variables were further explored by a series of correlational analyses.

Table 2.

Descriptive Statistics (N = 1,459).

Affordances	Presence	N	%	M	SD	Range

Expression	No	1,135	77.8
Expression	Yes	324	22.2
Self-clarification	No	1,091	74.8
Self-clarification	Yes	368	25.2
Social validation	No	1,416	97.1
Social validation	Yes	43	2.9
Relationship development	No	1,443	98.9
Relationship development	Yes	16	1.1
Social control	No	1,011	69.3
Social control	Yes	448	30.7
User
Network size				15,939.84	311,550.74	11,523,343
Tweet
Retweet count				71.10	1,633.60	52,627
Name mention	No	1,419	97.3
Name mention	Yes	11	2.7
Linguistic function
Sexual explicitness	Not sexually explicit	777	53.3
Sexual explicitness	Sexually explicit	682	46.7

RQ₁

The first research question inquired as to the most common types of social affordances sought in disclosing experiences of sexual assault on Twitter. Frequencies for the social affordance variables of expression, self-clarification, social validation, relationship development, and social control reveal that expression (22.2%), self-clarification (25.2%), and social control (30.7%) are the social affordances sought most often by Twitter users when they disclose experiences of sexual assault online (see Table 2). Social validation (2.9%) and relationship development (1.1%) were affordances rarely detected based on the reported frequencies. Based on this, the results for RQ₁ suggest that expression, self-clarification, and social control are the most common types of social affordances sought in disclosing experiences of sexual assault on Twitter.

Analysis

The first hypothesis predicted that the size of a user’s network would be positively associated with the level of sexual explicitness of the disclosure. As network size was positively skewed, we used a logarithmic (Log 10) transformation of this variable in our analysis. In addition, level of sexual explicitness was also nonnormally distributed, so we performed a nonparametric test of its association with network size. Spearman’s rho (r_s) was used to test H₁, which resulted in a statistically significant negative association between user network size and level of sexual explicitness of a disclosure (r_s = −.12, p < .001). The results disconfirmed H₁; however, an alternative association was observed and explored.

The second hypothesis predicted that the level of explicitness of an online disclosure would be positively associated with the number of times it was retweeted. As with network size, retweet count was also positively skewed, so a logarithmic (Log 10) transformation of this variable was used in our analysis. We also used Spearman’s rho (r_s) to test this hypothesis and found that disclosures that were not sexually explicit were retweeted more than disclosures that were sexually explicit (r_s = −.38, p < .001). Although these results disconfirmed H₂, we did observe an alternative association that was statistically significant.

The second research question asked whether online disclosures that reveal the perpetrator’s identity receive more retweets. As previously noted, to address the substantial positive skew of our original retweet count variable, we used a logarithmic (Log 10) transformation in our analysis. Results from a correlational test suggest a statistically insignificant, negative association between these variables (r = −.01, p > .05). Based on this finding, disclosures of sexual assault that name a perpetrator are not significantly more likely to receive a higher retweet count than those that do not provide a name.

Discussion

This study examined the relationship between functional self-disclosure and disclosures of experiences of sexual assault on Twitter. Specifically, the study examined the social affordances sought by Twitter users who disclose their experiences with sexual assault online as well as the association between the sexual explicitness of these disclosures, network size, and the retweet feature. As with most functional approaches to social media and disclosure, the understanding of affordances tends to begin with an understanding of the self.

The social affordance of expression being sought by Twitter users in their disclosures of sexual assault may be due to the connection between self-expression and narcissism. Expression as a social affordance refers to the cathartic function that the disclosure serves; the information provided by the discloser in his or her tweet expresses his or her feelings related to the experience of the sexual assault. The information offered by the discloser is not a negotiation; it is an expression of his or her own experience that does not intend to receive feedback. In this way, narcissism may contribute to expression being one of the social affordances sought by Twitter users in their sexual assault disclosures. Narcissism promotes a concern with maintaining a strong sense of self while also desiring to appear sociable (Jonason et al., 2012, 2014; Jones & Neria, 2015; Pailing et al., 2014; Rauthmann & Kolar, 2012).

This conceptualization may explain why expression is sought by Twitter users who disclose their assault experiences. In disclosing an experience of sexual assault to their social networks, Twitter users exhibit the narcissistic behavior of practicing sociability while prioritizing themselves through the provided information. Because expression as a social affordance only considers the individual discloser, Twitter users who disclose their sexual assaults to their social networks maintain the practice of narcissism while being part of a large social movement. The results of the first research question suggest that the social affordances sought from interpersonal disclosures, as suggested by Derlega and Grzelak (1979), are not sought in the online setting in the same way they are in face-to-face settings. Furthermore, in the online setting, people who disclose their experiences of sexual assault do not necessarily expect feedback to validate their self-concept, nor is it their primary intention to express their solidarity with others.

The first hypothesis predicted a positive association between network structure and level of sexual explicitness of an online disclosure. Impression management (Goffman, 1959) served as a foundation on which the hypothesis was structured. People generally intend to establish, manage, and maintain positive perceptions of themselves in the eyes of others (P. Rosenfeld et al., 1995; Singh & Vinnicombe, 2001; Tetlock & Manstead, 1985) and therefore seek to interactionally manage the impressions they present to those in their social networks (Goffman, 1959).

The results for the first hypothesis showed a statistically significant, negative association between the variables, indicating that larger Twitter networks were inversely associated with more sexually explicit disclosures. Analyzing the results through a lens of impression management may explain the results of this hypothesis and why small and medium-sized networks were found to be associated with disclosures that were sexually explicit as opposed to large networks. Twitter users who disclose their experiences of sexual assault online may withhold sexually explicit details in efforts to be involved with the conversation, but in such a way that does not reflect negatively on them, especially because the topic has garnered rapid public attention in the last year (Mendes et al., 2018; Tippett, 2018; Tolentino, 2018; Zarkov & Davis, 2018). Twitter users with large networks may also open themselves to more potential scrutiny or trolling from other users (Hardaker & McGlashan, 2016). A desire to avoid this scrutiny may support the association between small and medium-sized networks and sexual explicitness of sexual assault disclosures. Xie and Kang’s (2015) findings that SNS users disclose more information when their network sizes are larger support the first hypothesis, but the processes of data coding and analysis indicate that more information within an online disclosure does not equate to higher levels of sexual explicitness within a disclosure.

The #MeToo movement and conversations about sexual assault are socially engaging (Manikonda et al., 2018) despite the associated negativity and stigmatizations; as such, people have a desire to be part of something that has received public attention (Mendes et al., 2018; Tippett, 2018; Tolentino, 2018; Zarkov & Davis, 2018). Experiences of sexual assault have been stigmatized as deserved misfortunes of women and men whose choices of clothing, flirting styles, and expressions of sexuality prompted attacks (Bletzer & Koss, 2004; Ikizer et al., 2019; Jackson et al., 2017; Mendes et al., 2019). The toxicity on Twitter that is directed toward women may also contribute to the absence of sexual explicitness within disclosure tweets (Hardaker & McGlashan, 2016), especially in comparison with the support or encouragements that are likely to be expected in selective interpersonal disclosures (Paul et al., 2014). Women on Twitter experience threats of violence—both physical and sexual—and privacy violations, including sharing sexual images of women without their consent (Amnesty International, 2018). This discriminatory abuse on Twitter exacerbates the stigmatization of sexual assault, which leads to caution in revealing negative information on the platform. Sexual explicitness may be absent from the observed disclosures due to the abuse that is directed toward the women who seek out Twitter as a way to freely express themselves. The stigmatization of sexual assault as an incident that is deserved may provide an explanation for why Twitter users with large networks withhold sexually explicit details in their online disclosures of sexual assault.

In an effort to be active participants and advocates for the Me Too movement, Twitter users with small and medium-sized networks may disclose sexually explicit details of their sexual assaults because their networks that receive these tweets are not as expansive as other users’. Twitter users with large(r) networks on the platform may also disclose in an attempt to be active within the movement; however, the narratives these users share, according to our findings, reflect the converse of those with small and medium-sized networks. The data indicate Twitter users with large networks are not sharing sexually explicit details of their assaults. These users are willing to come forward (online) with disclosures that lack sexual explicitness. This may be attributed to a strong need for impression management (Goffman, 1959; Lin et al., 2014), given these users share information with hundreds to thousands of other users each time they post online. In addition, sexually explicit tweets receive attention in terms of retweets. One possibility of this finding may be that retweeting sexually explicit disclosure narratives allows certain Twitter users the opportunity to participate in the Me Too movement without directly outing themselves as a survivor and thus not risking reprisal from their networks and maintaining strong impression management. The findings of this study offer implications for continued research in the specific area of network analysis in the context of online disclosures of sexual assault.

Impression management (Goffman, 1959) may also help explain the results observed for the second hypothesis. This hypothesis expected a positive association between level of sexual explicitness of a disclosure and the number of times it was retweeted; however, the cross-tabulation revealed the inverse: Disclosures that were not sexually explicit were retweeted more than disclosures that were sexually explicit. When Twitter users are interested in the content of a tweet, they are more likely to retweet the post for their followers (Z. Yang et al., 2010). Retweeting may serve as some Twitter users’ primary means of communicating with their audiences instead of writing their own tweets; in this way, they still desire to maintain the public perceptions of themselves (P. Rosenfeld et al., 1995; Singh & Vinnicombe, 2001; Tetlock & Manstead, 1985) and the content that they choose to retweet may contribute to how they are perceived by their social networks. Retweeting disclosures of sexual assault that are not sexually explicit may allow certain users to participate in the online conversation in such a way that they are still well perceived by their social networks while avoiding the potential risk of alienating others with content that may be perceived as offensive by its sexually explicit nature.

The second research question sought to determine an association between a name mention within a disclosure and the retweet count of the disclosure. It was expected that revealing a name in a sexual assault disclosure would receive more attention within the platform, based on society’s desire to know details of offenders (Beshears et al., 2017), thus resulting in more retweets. However, the skewedness of the data indicates an inconclusive finding.

In addition to the development of this codebook, the results of this study provide an opportunity for the pursuit of future inquiries into what social media users intend to obtain from their online disclosures of sexual assault. This study extends the application of Derlega and Grzelak’s (1979) functional theory of self-disclosure to social media by expanding upon the Facebook studies by Bazarova and Choi (2014). The application of the theory to a particular type of self-disclosure, rather than to any shared information of the self, provides a foundational understanding of the social affordances that can motivate online disclosure behaviors within the context of sexual assault. The knowledge from this study may clarify previous explorations of online disclosures of sexual assault (Bogen et al., 2018; Bogen et al., 2019) and provide opportunities to refine future research questions and hypotheses to fit within the context of online disclosures of sexual assault. In addition, this study presents an opportunity to establish further theoretical implications. The acquired knowledge from this study that social media users seek the social affordances of expression, self-clarification, and social control in their disclosures offers an opportunity to examine online disclosures through a new theoretical lens. Specifically, future research might shift from focusing on the functional theory self-disclosure to examining online disclosures through a social sharing lens, expanding upon the research by Rodríguez Hidalgo et al. (2015) of the prosocial behavior of online sharing.

Some limitations are worthy of note. First, the study is limited due to the short list of cities that served as sites for data collection. A second limitation includes the use of only one SNS for data collection and coding. The selected list of key terms presents another limitation to this study. Extending the list to include more key terms might have resulted in a larger data set to analyze and, as such, generated different findings. Another limitation concerns the measure of sexual explicitness in this study, which was derived from prior measures used to measure sexual explicitness in mass media and subsequently adapted for the context of sexual assault. While the measure was intended to capture the level of sexually explicit detail of an online disclosure, it is worth noting that the study does not acknowledge that sexual assaults can be recounted in full detail without being sexually explicit. In this way, the measure of sexual explicitness of this study is limited. In addition, this study proceeded to code for the social affordance of expression according to the following codebook definition: The disclosure within the tweet serves a cathartic function; the discloser expresses his or her feelings or emotions related to the experience of the sexual assault. This study includes “cathartic” in the codebook according to the psychological definition of the term rather than assessing a user’s perception of what is cathartic. This approach presents a limitation as it is not possible to know whether a user who disclosed perceived the act of disclosing as personally cathartic.

Future research may consider focusing exclusively on retweeted content instead of original content. This type of examination of retweeted content on Twitter might provide insight as to why certain tweets are retweeted multiple times by users across different social networks. In addition, future inquiries may examine SNSs other than Twitter for disclosures of sexual assault. Specifically, analyzing disclosures on SNSs where there are less strict or no character limits may offer findings that are different than the findings from this Twitter analysis. Future research regarding disclosures of sexual assault might also focus on the offline conversations that are the results of online disclosures. Questions regarding the connections between online disclosures of sexual assault and the personality traits of users who disclose may be explored and answered through examinations of the conversations that occur offline. Specifically, future research could look further into the potential connection between users’ reasons for disclosing experiences of sexual assault and narcissism.

This study offers insight into the disclosing behaviors of users who have endured experiences of sexual assault. The popularity and social relevance of the #MeToo movement indicates its importance, as well as the importance of the topic of sexual assault, to the research of disclosure communications. At the time of this writing, no study had looked specifically at Twitter disclosures of sexual assault through a lens of functional self-disclosure and affordance-seeking; this study moves the literature in the area of disclosure research forward and offers a sound point for future endeavors. Regarding theory, this study provides some insight into the functional theory of self-disclosure by extending its components of social affordances to the specific context of disclosures of sexual assault on Twitter. Although the study’s hypotheses were only partially supported, the results offer an understanding of the types of affordances users may seek when disclosing their experiences online and the types of affordances that are insignificant to their disclosing behaviors. This study also presents possibilities for practical applications.

The results may be of particular interest to organizations whose missions are to help survivors of sexual assault. By understanding what users seek when they disclose online, organizational members may be better prepared in the interpersonal setting to offer counsel to survivors. In addition, the findings of this study could benefit designers and managers of social media platforms. By understanding the disclosing behaviors of users and being aware of the content (explicit and not explicit) they are including in their disclosures, designers and managers of social media platforms may be better equipped to respond to these users or be able to craft automatic responses that offer help resources specifically for survivors of sexual assault.

Footnotes

Acknowledgements

The authors are also grateful to two anonymous reviewers for their careful and insightful review of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conﬂicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This material is partially based upon work supported by the National Science Foundation under Grant No. 1416509, IBSS project titled “Spatiotemporal Modeling of Human Dynamics Across Social Media and Social Networks.” Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. This research is also partially supported by the School of Communication at San Diego State University.

ORCID iD

Bonnie-Elene Deal

Author Biographies

Bonnie-Elene Deal (MA, San diego state University) is a doctoral student of Communication Studies at the University of Miami. Her research interests include social media trends, big data, and health-related information sharing through social media.

Lourdes S. Martinez (PhD, University of Pennsylvania) is an associate professor of Communication at San Diego State University (SDSU). Her research interests include persuasion, social influence, and health-related information seeking and sharing on social media.

Brian H. Spitzberg (PhD/MA, University of Southern California) is a professor of Communication at the SDSU. His research interests include communication competence, the dark side of communication, stalking, and meme and social media diffusion.

Ming-Hsiang (Ming) Tsou (PhD, University of Colorado) is a professor in the Department of Geography, SDSU and the Founding Director of the Center for Human Dynamics in the Mobile Age (HDMA). His research interests are in human dynamics, social media, big data, visualization and cartography, and web GIS.

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“I Definitely Did Not Report It When I Was Raped . . . #WeBelieveChristine #MeToo”: A Content Analysis of Disclosures of Sexual Assault on Twitter

Abstract

Keywords

Disclosures of Sexual Assault

#MeToo and the Topic of Sexual Misconduct on Social Media

Theoretical Underpinnings: The Functional Theory of Self-Disclosure

Breadth, Depth, and Explicitness as They Relate to Theoretical Underpinnings

Method

Sample

Data Collection

Coding Procedure

Analytical Procedure

Results

Descriptive Statistics

RQ1

Analysis

Discussion

Footnotes

Acknowledgements

Declaration of Conflicting Interests

Funding

ORCID iD

Author Biographies

References

RQ₁