Abstract
Purpose
The purpose of this paper is to understand the self-protective behaviors of young adult cyberstalking victims and the factors that impact adoption of such behaviors.
Design/methodology/approach
This study used a sample of 880 young adults (18–25 years of age) who had experienced cyberstalking victimization within the previous 12 months. Data were collected through an online self-report survey hosted on Qualtrics from Amazon’s Mechanical Turk workers.
Findings
Results revealed that three-quarters of cyberstalking victims engaged in at least one form of self-protective behavior. The most commonly adopted self-protective behavior was blocking unwanted communications only (29%), while 40% of cyberstalking victims noted engaging in multiple forms of self-protective behaviors. While results varied across models, findings revealed that incident characteristics and respondent characteristics impacted the likelihood of engaging in self-protective behaviors after a cyberstalking victimization experience.
Originality/value
This study contributes to the literature examining the self-protective behaviors adopted by cyberstalking victims, which can shed light on other forms of cyber abuse and help explain victims’ low reporting rates to official sources (e.g. law enforcement).
Keywords
Citation
Fissel, E.R. and Lee, J.R. (2024), "Block? Delete? All of the above? The self-protective behaviors of young adult cyberstalking victims", Journal of Aggression, Conflict and Peace Research, Vol. 16 No. 4, pp. 301-315. https://doi.org/10.1108/JACPR-03-2024-0891
Publisher
:Emerald Publishing Limited
Copyright © 2024, Emerald Publishing Limited
Introduction
Cyberstalking, which is commonly defined as repeated unwanted pursuit behaviors conducted through communication technologies (e.g. Fissel et al., 2024; Nobles et al., 2014; Reyns et al., 2012), is a concerning public health problem experienced by both youth and adults around the world (e.g. Kaur et al., 2021; Morgan and Truman, 2022; Pereira et al., 2016; Statistics Canada, 2016; Vakhitova et al., 2020). In fact, a Pew Research Center study found that over 40% of US adults experienced some form of cyberstalking or online harassment within their lifetime (Duggan, 2017), which is estimated to be more common than offline stalking or harassment (Short et al., 2014; Vakhitova et al., 2020).
Cyberstalking has generated considerable attention from both researchers and practitioners due to its numerous health consequences, including depression, insecurity, sleeplessness and social withdrawal (Dreßing et al., 2014; Fissel and Reyns, 2020; Sheridan and Grant, 2007; Short et al., 2014, 2015; Tokunaga and Aune, 2017; Worsley et al., 2017; Vakhitova et al., 2020). Yet, results consistently reveal that only a small portion of those who experience cyberstalking report their victimization incident(s) to law enforcement (e.g. Brady et al., 2023; Fissel, 2021; Reyns and Englebrecht, 2010). Given the various harms and lack of official reporting or formal help-seeking behavior, it is plausible that victims may adopt a variety of self-protective measures to resolve their current incident(s) and prevent future victimization from occurring (Averdijk, 2011; Vakhitova et al., 2020). To that end, studies revealed that many cyberstalking victims do not engage in self-protective behaviors (e.g. Vakhitova et al., 2020). In fact, one study revealed that only 40% of victims blocked or deleted the individual responsible for the attack and withdrew from the online platform (e.g. social media) to avoid further contact with the perpetrator (Duggan, 2017).
Though numerous studies exploring offline stalking victims’ willingness to adopt self-protective behaviors have been conducted (e.g. Baum et al., 2009; Buhi et al., 2009; Reyns and Englebrecht, 2010), research assessing cyberstalking victims’ adoption of self-protective behaviors have been scant. While research has found some overlap in how victims of cyberstalking and offline stalking respond to their victimization incident (e.g. ignoring the victimization, confronting the offender; Sheridan and Grant, 2007), studies suggest victims may also adopt medium-specific measures that are unique to their incident, such as making their online social media profiles private or purchasing caller identification services to minimize their negative experiences involving telephone communications (Spitzberg et al., 1998; Vakhitova et al., 2020).
Given the minimal research examining the self-protective behaviors taken by cyberstalking victims and the factors (e.g. incident traits, respondent characteristics) predicting victims’ adoption of such behaviors, the current study has two main objectives. First, we seek to identify the different types of self-protective behaviors taken by young adults who have experienced cyberstalking victimization. Second, the study seeks to explore both the incident and respondent characteristics that may impact adoption of self-protective behaviors. This aim will provide a deeper understanding of why certain individuals and victimization experiences elicit greater self-protective responses following the incident, along with potentially providing insight into research findings on other post-victimization experiences (e.g. reporting and help-seeking).
Brief theoretical framework of self-protective behaviors
Criminological research has often examined victims’ willingness to adopt self-protective behaviors using rational choice frameworks as opposed to interpreting these actions as spontaneous responses to criminal victimization (see Guerette and Santana, 2010; Tark and Kleck, 2004; Powers and Simpson, 2012). Specifically, numerous studies have argued that self-protective behaviors are an outcome of individuals calculating the advantages and disadvantages of adjusting their patterned behaviors following a victimization incident (see Vakhitova et al., 2020). Research has found that self-protective behaviors are more likely to be taken by victims when the benefits of preventing subsequent victimization (or resolving current incidents) are perceived as greater than the costs associated with implementing the protective behavior (McCarthy and Chaudhary, 2014; Vakhitova et al., 2020). Similarly, individuals may choose not to engage in any type of self-protective behavior if performing the action outweighs the costs associated with the current and/or subsequent victimization incident (see Skogan, 1981). Under this framework, it is presumed that victims are more likely to adopt self-protective behaviors for incidents that are perceived to be more severe and harmful relative to those that are less threatening and consequential (Vakhitova et al., 2020).
Cyberstalking victimization and self-protective behaviors
Although limited in scope, several studies examining the post-victimization behaviors undertaken by cyberstalking victims have recently emerged. For one, Fissel (2021) examined cyberstalking victims’ adoption of formal protection behaviors (e.g. reporting victimization to law enforcement), whereas Sheridan and Grant (2007) identified several informal self-protective behaviors that cyberstalking victims engaged in, including changing their employment or course of study, increasing their security, changing their identity and reducing their social activity. While these studies assessed the self-protective behaviors adopted by cyberstalking victims, they mostly explored the offline behaviors taken by these actors.
Vakhitova and colleagues (2020), however, identified six online self-protective behaviors that cyber abuse [1] victims adopted in response to their victimization experiences, including adjusting privacy settings, blocking specific contacts, improving cybersecurity measures, engaging in self-censorship, refraining from sharing personal information online and avoiding technology or social media. Findings revealed that approximately 40% of those who experienced cyber abuse engaged in at least one self-protective behavior. The most common actions were adjusting online privacy settings and avoiding technology/social media, whereas the least common behavior was improving cybersecurity measures (Vakhitova et al., 2020).
Role of incident characteristics
Offense seriousness has been consistently identified as a significant predictor impacting victims’ decision-making process (see Gottfredson and Gottfredson, 1988), including engagement in various reporting and protective behaviors. The literature has operationalized offense seriousness in a variety of ways, including the duration of the victimization incident(s), feelings of fear and its associated consequences. Overall, the literature suggests that cyberstalking incidents that are more severe (Vakhitova et al., 2020), including being longer in duration (Fissel, 2021; Nobles et al., 2014), resulting in fear (Nobles et al., 2014) and resulting in health consequences (e.g. increased substance use, trouble sleeping; Fissel, 2021) are more likely to result in the victim engaging in formal reporting, help-seeking, or other self-protective behaviors.
The victim-offender relationship has also been found to impact individuals’ adoption of self-protective behaviors. For instance, individuals who did not know the identity of their perpetrators were twice as likely to adopt self-protective behaviors relative to those who knew their offenders (Vakhitova et al., 2020). Existing literature has argued that victims may not employ self-protective measures in circumstances where the offender is a known party (e.g. current intimate partner) due to fears of damaging the relationship and/or fears that their actions will put them at even greater levels of risk and harm (Ullman, 1997). Relatedly, several sexual assault studies have found that victims are less likely to adopt self-protective behaviors against intimate persons (see Bart and O'Brien, 1985; Clay-Warner, 2003; Harrington and Leitenberg, 1994; Ullman and Siegel, 1993; see Powers, 2014 for exception) possibly because of an inability to immediately recognize the signs of the victimization. Alternatively, research exploring non-sexual victimization revealed that victims are more likely to adopt self-protective behaviors when the offender is an intimate partner (Bachman and Carmody, 1994). While findings are inconclusive, the victim-offender relationship status is an important factor to consider, as it captures the intensity of the grievance, harm and potential motivation for action (see Felson and Messner, 2000; Powers, 2014; Tedeschi and Nesler, 1993).
Role of respondent characteristics
Though research examining individuals’ demographic characteristics and victims’ willingness to adopt self-protective actions are inconclusive, limited studies have found that older individuals were more likely to adopt some form of self-protective behavior than younger individuals (Vakhitova et al., 2020). It may be that older individuals are better informed and more aware of different self-protective techniques than younger victims (Vakhitova et al., 2019). In addition, race and employment status were found to be non-significant predictors of adopting self-protective behaviors post-victimization (e.g. Atkeson et al., 1989; Powers, 2014; Vakhitova et al., 2020). While findings remain inconclusive, studies suggest males are more likely to engage in self-protective behaviors given the relative physiological size differences between men and women. That is, men may be more willing to use self-protective behaviors that reflect their physical advantage (see Block and Skogan, 1984; Felson, 1996; Powers, 2014). Alternatively, the disproportionate victimization of women across various crime types may demonstrate a greater willingness for women to adopt self-protective behaviors to resolve their victimization incidents (Powers, 2014). Further, perceptions that others will not take their victimization seriously, or that bystanders will not intervene during a victimization incident, may impact women’s willingness to take self-protective behaviors.
Despite the growth in empirical studies examining cyberstalking victims’ willingness to enact self-protective behaviors (see Baum et al., 2009; Buhi et al., 2009; Reyns and Englebrecht, 2010), research exploring the relationship between respondents’ characteristics and their adoption of self-protective behaviors remain scarce. Specifically, less is known about whether offline respondent characteristics (e.g. age, gender, race) impact cyberstalking victims’ adoption of self-protective behaviors given that offline traits may be less relevant within online environments. For instance, a victim’s physiological size may be less relevant in online incidents, as the offense is committed in an environment that neutralizes certain offline factors. Although various points of overlap between cyberstalking and offline stalking victims exist (see Sheridan and Grant, 2007), results are inconclusive regarding whether individuals’ offline demographic traits significantly predict cyberstalking victims’ adoption of self-protective actions.
Current study
Although research examining cyberstalking behaviors has increased, limited attention has been placed on the self-protective behaviors adopted by cyberstalking victims. This has left a gap in knowledge related to the behaviors that victims engage in after a cyberstalking incident, which can shed light on the underreporting to law enforcement that has been a consistent finding in existing literature. As such, the current study builds on the limited extant research by examining both online and offline forms of self-protective behaviors, along with the factors that impact adoption of such behaviors. Specifically, the current study was guided by the following research questions:
RQ1. What types of self-protective behaviors do cyberstalking victims engage in?
RQ2. Do characteristics of the cyberstalking incident influence the types of self-protective behaviors adopted?
RQ3. Do respondent characteristics impact the types of self-protective behaviors used?
Methods
Procedures and participants
Data were collected through an online self-report survey from November 2017 to January 2018 as part of a larger study on cyberstalking victimization. To be eligible for the study, individuals were required to have an active Mechanical Turk (MTurk) worker account, be between 18 and 25 years of age, reside in the US and be an English speaker. Eligible participants were provided with a link to an online questionnaire, which was hosted on Qualtrics. Individuals were included in the sample if they successfully completed the online questionnaire (e.g. entered the correct completion code). Data collection ended when the target sample size was reached (n = 1,500).
Given the current study’s scope and objective, analyses were conducted on a subsample of individuals who were behaviorally cyberstalked within the 12 months prior to data collection. Given the debate over the fear standard [2] (Dietz and Martin, 2007; Fissel et al., 2024; Pereira and Matos; 2016), the current study opted to use a measure of behavioral cyberstalking victimization. Participants were asked to indicate if they had experienced any of the seven behaviors through communication technologies within the past 12 months, including:
unwanted contact or attempted contact;
harassment or annoyance;
unwanted sexual advances;
threats of harm;
spied on or monitored activities;
whereabouts tracked; and
inappropriate, unwanted or personal posts or threatened posts.
Those who indicated experiencing any of the aforementioned behaviors were then asked the number of times they experienced each type of unwanted contact or behavior within the 12 months. Response options consisted of 1 time, 2 times, 3–6 times, 7–10 times or more than 10 times. To meet the criteria for cyberstalking victimization in the current study, respondents had to experience repeated pursuit behaviors, meaning either one of the unwanted behaviors two or more times (e.g. two or more experiences of unwanted contact or attempted contact) or at least two unwanted behaviors at least one time each (e.g. one experience of unwanted contact or attempted contact and one experience of whereabouts tracked).
Of the 1,500 participants who completed the online questionnaire, approximately 59% (n = 880) of the full sample met the criteria for cyberstalking victimization and were included in the analytic sample. Among those in the analytic sample, respondents’ age ranged from 18 to 25 years (M = 22.78, SD = 1.82), while approximately 69% (n = 609) of the sample identified as women, followed by men (26%; n = 230) and another gender identity (5%; n = 41). With respect to race, 64% (n = 566) identified as white or Caucasian, 11% (n = 92) as multiracial, 10% (n = 85) as black or African American, 8% (n = 72) as another race and 7% (n = 85) as Asian. Nearly half (49%; n = 434) of the sample indicated high school as their highest level of education, whereas 45% (n = 393) had obtained an undergraduate degree and 6% (n = 53) had a graduate or professional degree.
Measures
Self-protective behaviors. A series of dependent variables were created to capture the various forms of self-protective behaviors that cyberstalking victims engaged in. Respondents who met the criteria for cyberstalking victimization were presented with the following prompt: “There are things that people might try to do to protect themselves or stop unwanted contacts or behaviors from continuing. Indicate if you have done any of the following. Please select all that apply.” Eight responses were provided, including:
changed your daily activities;
blocked unwanted calls, messages or other communications;
deleted online accounts;
taken self-defensive actions or other security measures;
changed your personal information (e.g. social security number);
changed your contact information (e.g. phone number, e-mail address);
applied for a restraining, protective or no-contact order; and
I did nothing.
Nine dichotomous variables were created based on these response options. The first variable represented engaging in any of the initial seven self-protective behaviors above (0 = engaged in any; 1 = engaged in no behaviors). Then, seven additional variables represented engaging only in each of the first seven self-protective behaviors above (0 = did not engage in behavior; 1 = engaged in behavior) and were labeled as such: changed daily activities only; blocked only; deleted accounts only; self-defensive actions only; changed personal information only; changed contact information only; and applied for restraining order only. A ninth variable was created that represented engaging in multiple (i.e. at least two) self-protective behaviors (0 = did not engage in multiple behaviors; 1 = engaged in multiple behaviors).
Incident characteristics. Respondents were asked to indicate if they experienced fear (0 = did not experience fear; 1 = experienced fear) or experienced a substantial emotional response (0 = did not experience substantial emotional response; 1 = experienced substantial emotional response) as a result of the unwanted pursuit behaviors. Number of incidents was also created by taking the sum of the cyberstalking behaviors experienced by each respondent.
Respondents were also asked to indicate the relationship they had with the offender at the time of the cyberstalking incident. A series of dummy variables were created to capture the victim-offender relationship, including current intimate partner, former intimate partner, other non-stranger and stranger (reference group). Finally, respondents were asked if they experienced health consequences as a result of the unwanted pursuit behaviors. Specifically, they were asked to indicate if they experienced increased drug use, increased alcohol use, headaches or stomachaches, eating problems or disorders and nightmares or trouble sleeping. Responses were summed with higher values representing a greater number of health consequences.
Respondent characteristics. Age was measured as a continuous variable (18–25), whereas gender identity was captured by asking respondents to indicate which best described their identity. A series of dummy variables were created and labeled man (reference group), woman and another identity. Likewise, race was measured by asking respondents to indicate which best represented their racial identity. A series of dummy variables were created and labeled White or Caucasian (reference group), Black or African American, Asian, Multiracial and another race. Education was captured by asking respondents to indicate the highest level of education they had completed. A series of dummy variables were created and labeled as high school (reference group), undergraduate degree (includes both associate and bachelor’s degrees) and graduate or professional degree.
Analytic strategy
First, descriptive statistics for all variables of interest were generated. Second, bivariate correlations were calculated between the independent and dependent variables (available upon request). Third, a series of binary logistic regression models were estimated to determine the impact of the independent variables on each type of self-protective behavior. Statistical models are only displayed for three of the self-protective behaviors (i.e. engaged in any self-protective behavior; blocked unwanted communications only; and multiple self-protective behaviors) because of the small number of participants engaging exclusively in the other forms. P-values equal to or less than 0.05 were considered statistically significant in all analyses. Prior to analyses, multicollinearity diagnostics were assessed and deemed not to be an issue.
Results
Descriptive statistics
Approximately three-quarters (74.52%) of cyberstalking victims in the current sample engaged in at least one form of self-protective behavior. Blocking unwanted activities was the most commonly identified self-protective behavior used by respondents, with 64% (n = 565) noting they engaged in this activity. The second most commonly adopted self-protective behavior was deleting online accounts (21.93%; n = 193), closely followed by changing daily activities (20.45%; n = 180). Forty percent (n = 352) of cyberstalking victims noted engaging in multiple forms of self-protective behaviors. When examining number of forms, 20% (n = 180) engaged in two types, 11% (n = 100) engaged in three, 6% (n = 54) engaged in four and a small percentage engaged in 5, 6 or 7 types (see Table 1 for details).
When focusing on the mutually exclusive dependent variables, the most commonly identified self-protective behavior was blocking unwanted communications only (n = 251; 28.52%). All other forms of self-protective behaviors that were exclusively adopted (i.e. not in combination with other forms) were done so by a small percentage of respondents, including just under 2% (n = 17) only deleting online accounts and only changing their daily activities (n = 16), and less than 1% each engaging in self-defensive actions only, changing personal information only, changing contact information only and applying for restraining order only (see Table 2 for details).
Multivariate statistics
Table 3 presents the results of the relationship between incident and respondent characteristics on engaging in any form of self-protective behavior. As displayed, experiencing fear as a result of the cyberstalking victimization incident was significantly and positively associated with engaging in any form of self-protective behavior (OR = 2.00). Those who experienced a greater number of cyberstalking incidents were also more likely to engage in any form of self-protective behavior (OR = 1.02), as were those who experienced more health consequences (OR = 2.54). Finally, those who were cyberstalked by a current intimate partner were less likely than those cyberstalked by a stranger to engage in any form of self-protective behavior (OR = 0.39).
Table 4 displays the results of the relationship between the incident and respondent characteristics on only engaging in blocking unwanted communication. As presented, the study found that experiencing fear as a result of the cyberstalking incident was significantly associated with blocking unwanted communication, such that those who experienced fear were less likely to only block unwanted communication (OR = 0.60). Additionally, those who experienced more health consequences were significantly less likely to only block unwanted communication (OR = 0.70). Finally, Asian respondents – relative to White respondents – were significantly less likely to only block unwanted communication (OR = 0.51).
Table 5 presents the results of the incident and respondent characteristics on engaging in multiple forms of self-protective behaviors. Experiencing fear as a result of the cyberstalking behaviors was significantly associated with engaging in multiple self-protective behaviors, such that those who experienced fear were more likely to engage in multiple self-protective behaviors (OR = 2.13). The number of incidents encountered (OR = 1.02) and experiencing health consequences (OR = 2.09) were also positively associated with engaging in multiple self-protective behaviors. With respect to respondent characteristics, older respondents were more likely to engage in multiple self-protective behaviors (OR = 1.13) than younger respondents. Additionally, those who identified as multiracial were more likely to engage in multiple self-protective behaviors relative to those who identified as White (OR = 1.70).
Discussion
Research has consistently revealed that individuals who experience cyberstalking victimization often do not report their experiences to law enforcement (Brady et al., 2023; Fissel, 2021; Reyns and Englebrecht, 2010). A potential explanation for victims’ unwillingness to interact with law enforcement may be that individuals are engaging in self-protective behaviors and prefer to enact informal help-seeking behaviors. The current study sought to better understand the self-protective behaviors of cyberstalking victims and the characteristics that impact individuals’ adoption of these behaviors. Various insights can be drawn from our findings.
First, our research revealed that approximately 75% of all cyberstalking victims engaged in some form of self-protective behavior, which is significantly higher than what has been found in previous research (Vakhitova et al., 2020). The most common form of self-protective behavior reported was blocking unwanted communications, with approximately 64% of cyberstalking victims noting engaging in this activity. In addition, 40% of cyberstalking victims reported engaging in multiple forms of self-protective behaviors. Specifically, most individuals engaging in multiple forms of self-protective behaviors adopted two or three types of action. When exploring the mutually exclusive items (i.e. one type of self-protective behavior only), the most commonly adopted behavior was engaging in only blocking unwanted communications (28.52%). It could be argued that blocking unwanted contacts is the fastest and easiest way to get communication to stop and serves as the first line of defense. Interestingly, a quarter of cyberstalking victims did not engage in any form of self-protective behavior. It may be the case that individuals who choose not to adopt self-help measures may not believe their victimization incident is serious enough to warrant preventive action (Vakhitova et al., 2020). Even if the actual victimization incident is serious, individuals may not feel the need to adopt a protective action if they do not perceive their victimization as necessitating a preventative response.
It could also be the case that victims are unwilling to engage in self-protective behaviors if they do not recognize the cyberstalking behavior as problematic, which may be more salient among victims who have a former or current intimate relationship with their perpetrator. This notion is supported by previous research that has found that individuals cyberstalked by a current intimate partner most commonly perceived their offender to be motivated by feelings of affection (Fissel, 2021). The same study found that those who were cyberstalked by a former intimate partner perceived the offender to be motivated by feelings of rejection. Taken together, victims may not consider their offenders’ behavior as harmful because they normalize it as an expression of emotions within an existing or former intimate relationship. In this way, victims may underestimate the imminent harms and dangers associated with the victimization incident and choose not to exercise self-protective behaviors. Public awareness and education campaigns regarding the warning signs and behavioral signals of cyberstalking victimization could serve as an important first step to resolving these more complicated situations.
In terms of the second research question, several incident level characteristics were significantly associated with cyberstalking victims’ adoption of self-protective behaviors. For one, experiencing fear was a consistently significant predictor across all three models. Specifically, those who experienced fear as a result of their cyberstalking experience were significantly more likely to engage in multiple forms of self-protective behaviors. Even though cyberstalking victims may experience harm differently based on each incident’s unique characteristics, including those who experience harm without necessarily encountering feelings of fear (see Fissel et al., 2024), our findings revealed that victims’ decision to adopt self-protective behaviors were significantly associated with encountering fearful emotions. While this does not imply that all cyberstalking incidents contain fear-inducing experiences, it highlights the importance of fear in increasing one’s willingness to adopt self-protective behaviors to resolve their victimization incident(s). Given that fear may be an indication of offense seriousness (see Reyns and Englebrecht, 2010), it is unsurprising to note that cyberstalking victims would adopt multiple self-protective behaviors to stop undesired interactions.
Health consequences also yielded statistically significant findings across all three models. In fact, cyberstalking victims who experienced greater health consequences were more likely to engage in multiple forms of self-protective behaviors. Similar to experiencing fear, since health consequences may be an indication of offense seriousness, it is reasonable to expect those experiencing more health consequences to adopt multiple self-protective behaviors to resolve their victimization incident.
Finally, related to our third research question, our analyses found that respondents’ characteristics did not yield consistent findings across the models. Race was the only significant respondent characteristic in our study, though it was only significant in two of the models with varying impact across those models. Specifically, Asian respondents were more likely to engage in blocking unwanted communications only relative to White respondents, while multiracial respondents were more likely to engage in multiple self-protective behaviors relative to White respondents. Given both our inconclusive findings and those presented in the extant literature, we urge future researchers to explore this racial dynamic (among other respondent characteristics) more fully in subsequent studies.
Limitations and future directions
Despite the current study’s contributions to the cyberstalking literature, it is not void of limitation. First, the current study did not measure how the cyberstalking incident unfolded. For instance, we did not inquire about the online platform (e.g. Facebook, Instagram, YouTube) nor the specific type of cyberstalking victimization experienced (e.g. sending threatening or disturbing messages through email; monitoring one’s activities on social media; posting private information about the victim on online forums). It may be the case that certain cyberstalking incidents are perceived as more harmful and evoke a greater amount of self-protective behaviors. Relatedly, it may be easier to block perpetrators on social media platforms than to prevent offenders from posting harmful information in public online spaces (Vakhitova et al., 2020). Future research would benefit from including more nuanced measures of the cyberstalking incident to enhance deeper comprehension of both the victimization incident and its relation to victims’ adoption of self-help behaviors.
The current study was also derived from a self-report survey inquiring cyberstalking victims about their experiences post-victimization. Self-report surveys suffer from several biases, including recall issues and the inability to verify the details of the incident. Given that our measures were self-reported indicators of victimization, there may be incidents that were unaccounted for in the current study since not all victimizations come to the attention of the victim. Further, there was no way to verify whether the cyberstalking offenses were committed by strangers or known individuals (e.g. current/former intimate partners). While containing limitations, self-report data are associated with lower interviewer-induced measurement errors and social desirability bias (see Baker et al., 2010; Chang and Krosnick, 2009; Kreuter et al., 2008; Sue and Ritter, 2012).
In addition, our sample is neither representative of the US adult population nor of cyberstalking victims. The cross-sectional nature of the current study is also a notable limitation, as the study was unable to determine whether the adoption of self-protective behaviors was a result of the victimization. Longitudinal studies would provide deeper insight into whether self-protective behaviors are adopted by individuals at the start of their victimization incident or after a more serious attack is made on the victim. Future studies would benefit from implementing longitudinal designs to examine the directionality of the relationship between adopting self-protective behaviors and the cyberstalking victimization incident.
Conclusions
Our findings indicated that the majority of cyberstalking victims engaged in at least one form of self-protective behavior, though a meaningful proportion of victims did not (25.68%). Additionally, we found that several incident and respondent characteristics were significant predictors of victims’ adoption of self-protective behaviors following their cyberstalking incident. Improving individuals’ understanding around the severity and consequences of cyberstalking victimization may increase their situational awareness, which may lead to greater adoption of self-protective behaviors (see Maimon et al., 2022). These self-protective behaviors may have the effect of both resolving the current cyberstalking incident and preventing other non-cyberstalking incidents from arising (i.e. adjusting privacy settings may lower one’s risk of experiencing both cyberstalking and fraud victimization). Implications also include educating individuals on the importance and utility of adopting self-protective measures regardless of the incident’s severity, as cyberstalking incidents can escalate quickly.
Cyberstalking victims’ engagement in self-protective behaviors by type (n = 880)
Form of self-protective behavior | n | % |
---|---|---|
Engaged in any self-protective behavior | 654 | 74.32 |
Changed daily activities | 180 | 20.45 |
Blocked unwanted activities | 565 | 64.20 |
Deleted online accounts | 193 | 21.93 |
Took self-defensive actions | 90 | 10.23 |
Changed personal information | 54 | 6.14 |
Changed contact information | 162 | 18.41 |
Applied for restraining order | 29 | 3.30 |
Multiple self-protective behaviors | 352 | 40.00 |
2 types | 180 | 20.45 |
3 types | 100 | 11.36 |
4 types | 54 | 6.14 |
5 types | 10 | 1.14 |
6 types | 5 | 0.57 |
7 types | 3 | 0.34 |
Source: Created by authors
Descriptive statistics (n = 880)
Variable | Coding | n (%) | M (SD) |
---|---|---|---|
Dependent variables | |||
Engaged in any self-protective behavior | 0–1 | 654 (74.32%) | – |
Changed daily activities only | 0–1 | 16 (1.82%) | – |
Blocked unwanted communication only | 0–1 | 251 (28.52%) | – |
Deleted online accounts only | 0–1 | 17 (1.93%) | – |
Took self-defensive actions only | 0–1 | 8 (0.91%) | – |
Changed personal information only | 0–1 | 3 (0.34%) | – |
Changed contact information only | 0–1 | 5 (0.57%) | – |
Applied for restraining order only | 0–1 | 2 (0.23%) | – |
Multiple self-protective behaviors | 0–1 | 352 (40.00%) | – |
Independent variables | |||
Incident characteristics | |||
Experienced fear | 0–1 | 283 (32.16%) | – |
Experienced substantial emotional response | 0–1 | 442 (50.23%) | – |
Number of incidents | 2–77 | – | 10.59 (12.30) |
Victim-offender relationship | |||
Current intimate partner | 0–1 | 46 (5.23%) | – |
Former intimate partner | 0–1 | 178 (20.23%) | – |
Other non-stranger | 0–1 | 352 (40.00%) | – |
Stranger (reference group) | 0–1 | 304 (34.55%) | – |
Experienced health consequences | 0–5 | – | 0.78 (1.17) |
Respondent characteristics | |||
Age | 18–25 | – | 22.78 (1.82) |
Gender identity | |||
Man (reference group) | 0–1 | 230 (26.14%) | – |
Woman | 0–1 | 609 (69.20%) | – |
Another identity | 0–1 | 41 (4.66%) | – |
Race | |||
White or Caucasian (reference group) | 0–1 | 566 (64.32%) | – |
Black or African American | 0–1 | 85 (9.66%) | – |
Asian | 0–1 | 65 (7.39%) | – |
Multiracial | 0–1 | 92 (10.45%) | – |
Another race | 0–1 | 72 (8.18%) | – |
Education | |||
High school (reference group) | 0–1 | 434 (49.32%) | – |
Undergraduate degree | 0–1 | 393 (44.66%) | – |
Graduate or professional degree | 0–1 | 53 (6.02%) | – |
Source: Created by authors
Binary logistic regression – engaged in any self-protective behavior
Independent variables | b (SE) | p-value | OR | OR 95% CI |
---|---|---|---|---|
Incident characteristics | ||||
Experienced fear | 0.69 (0.26) | 0.007 | 2.00 | [1.20, 3.32] |
Experienced substantial emotional response | 0.20 (0.20) | 0.320 | 1.22 | [0.82, 1.82] |
Number of incidents | 0.02 (0.01) | 0.044 | 1.02 | [1.00, 1.05] |
Victim-offender relationship (stranger) | ||||
Current intimate partner | −0.93 (0.44) | 0.035 | 0.39 | [0.17, 0.94] |
Former intimate partner | 0.20 (0.26) | 0.439 | 1.22 | [0.74, 2.01] |
Other non-stranger | 0.09 (0.19) | 0.647 | 1.09 | [0.75, 1.58] |
Experienced health consequences | 0.93 (0.16) | 0.000 | 2.54 | [1.84, 3.49] |
Respondent characteristics | ||||
Age | 0.09 (0.05) | 0.070 | 1.09 | [0.99, 1.21] |
Gender identity (man) | ||||
Woman | 0.28 (0.19) | 0.143 | 1.32 | [0.91, 1.92] |
Another identity | 0.29 (0.48) | 0.542 | 1.34 | [0.52, 3.45] |
Race (white) | ||||
Black or African American | 0.22 (0.32) | 0.492 | 1.25 | [0.66, 2.36] |
Asian | −0.39 (0.31) | 0.208 | 0.68 | [0.37, 1.24] |
Multiracial | 0.28 (0.32) | 0.380 | 1.32 | [0.71, 2.47] |
Another race | −0.47 (0.29) | 0.103 | 0.62 | [0.35, 1.10] |
Education (high school) | ||||
Undergraduate degree | −0.08 (0.18) | 0.661 | 0.92 | [0.65, 1.32] |
Graduate or professional degree | −0.27 (0.40) | 0.503 | 0.76 | [0.35, 1.67] |
Constant | −1.98 (1.11) | 0.076 | 0.14 | |
−2 Log-likelihood | 846.54 | |||
Model x2 | 156.13*** | |||
Nagelkerke R2 | 0.239 |
Source: Created by authors
Binary logistic regression – blocked unwanted communication only
Independent variables | b (SE) | p-value | OR | OR 95% CI |
---|---|---|---|---|
Incident characteristics | ||||
Experienced fear | −0.50 (0.22) | 0.019 | 0.60 | [0.40, 0.92] |
Experienced substantial emotional response | 0.17 (0.19) | 0.346 | 1.19 | [0.83, 1.71] |
Number of incidents | −0.01 (0.01) | 0.174 | 0.99 | [0.97, 1.01] |
Victim-offender relationship (stranger) | ||||
Current intimate partner | −0.46 (0.48) | 0.344 | 0.63 | [0.25, 1.61] |
Former intimate partner | 0.26 (0.22) | 0.239 | 1.30 | [0.84, 1.99] |
Other non-stranger | −0.05 (0.18) | 0.762 | 0.95 | [0.67, 1.34] |
Experienced health consequences | −0.35 (0.10) | <0.001 | 0.70 | [0.58, 0.85] |
Respondent characteristics | ||||
Age | 0.00 (0.05) | 0.943 | 1.00 | [0.92, 1.10] |
Gender identity (man) | ||||
Woman | 0.34 (0.19) | 0.065 | 1.41 | [0.98, 2.04] |
Another identity | 0.40 (0.41) | 0.330 | 1.49 | [0.67, 3.32] |
Race (white) | ||||
Black or African American | −0.22 (0.28) | 0.430 | 0.80 | [0.46, 1.39] |
Asian | −0.68 (0.34) | 0.048 | 0.51 | [0.26, 1.00] |
Multiracial | −0.20 (0.27) | 0.456 | 0.82 | [0.48, 1.39] |
Another race | −0.12 (0.29) | 0.673 | 0.89 | [0.50, 1.56] |
Education (high school) | ||||
Undergraduate degree | 0.00 (0.17) | 0.991 | 1.00 | [0.73, 1.39] |
Graduate or professional degree | −0.46 (0.41) | 0.262 | 0.63 | [0.28, 1.41] |
Constant | −0.75 (1.03) | 0.465 | 0.47 | |
−2 Log-likelihood | 992.28 | |||
Model x2 | 59.89*** | |||
Nagelkerke R2 | 0.094 |
Source: Created by authors
Binary logistic regression – multiple self-protective behaviors
Independent variables | b (SE) | p-value | OR | OR 95% CI |
---|---|---|---|---|
Incident characteristics | ||||
Experienced fear | 0.76 (0.20) | < 0.001 | 2.13 | [1.45, 3.14] |
Experienced substantial emotional response | 0.15 (0.19) | 0.439 | 1.16 | [0.80, 1.68] |
Number of incidents | 0.02 (0.01) | 0.015 | 1.02 | [1.00, 1.04] |
Victim-offender relationship (stranger) | ||||
current intimate partner | −0.07 (0.41) | 0.856 | 0.93 | [0.42, 2.06] |
former intimate partner | −0.14 (0.23) | 0.547 | 0.87 | [0.55, 1.37] |
other non-stranger | 0.08 (0.19) | 0.677 | 1.08 | [0.75, 1.56] |
Experienced health consequences | 0.74 (0.09) | < 0.001 | 2.09 | [1.75, 2.50] |
Respondent characteristics | ||||
Age | 0.12 (0.05) | 0.012 | 1.13 | [1.03, 1.24] |
Gender identity (man) | ||||
Woman | 0.18 (0.19) | 0.354 | 1.19 | [0.82, 1.73] |
Another identity | −0.13 (0.41) | 0.743 | 0.87 | [0.39, 1.95] |
Race (white) | ||||
Black or African American | 0.19 (0.27) | 0.488 | 1.21 | [0.71, 2.06] |
Asian | −0.02 (0.32) | 0.953 | 0.98 | [0.53, 1.83] |
Multiracial | 0.53 (0.27) | 0.046 | 1.70 | [1.01, 2.87] |
Another race | −0.30 (0.31) | 0.330 | 0.74 | [0.40, 1.36] |
Education (high school) | ||||
Undergraduate degree | −0.26 (0.17) | 0.124 | 0.77 | [0.55, 1.08] |
Graduate or professional degree | 0.12 (0.37) | 0.739 | 1.13 | [0.55, 2.35] |
Constant | −4.33 (1.11) | < 0.001 | 0.01 | |
−2 Log-likelihood | 945.24 | |||
Model x2 | 239.26*** | |||
Nagelkerke R2 | 0.322 |
Source: Created by authors
Notes
Vakhitova et al. (2020) used the general term “cyber abuse” to refer to both cyberstalking and online harassment behaviors jointly. Specifically, the study measured “cyber abuse” by asking respondents to indicate whether they had “ever experienced any form of cyber stalking or cyber harassment directed at your personally? By cyber stalking and cyber harassment, we mean the use of the Internet or other technological means (cell phones, gaming devices, etc.) to stalk or harass.”
The fear-standard is the argument that individuals must experience feelings of fear from their victimization incident(s) to be considering a stalking victim. Several scholars have argued that an absence of fear by the victim makes the definition of stalking largely indistinguishable from harassment. In contrast, other scholars have argued that this requirement severely underrepresents the number of individuals experiencing repeated pursuit behaviors who would benefit from criminal justice and victim service resources (see Dietz and Martin, 2007; Fissel et al., 2024; Pereira and Matos, 2016).
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Further reading
Reyns, B.W. and Englebrecht, C.M. (2014), “Informal and formal help-seeking decisions of stalking victims in the United States”, Criminal Justice and Behavior, Vol. 41 No. 10, pp. 1178-1194.
Siegel, J.M., Sorenson, S.B., Golding, J.M., Burnam, M.A. and Stein, J.A. (1989), “Resistance to sexual assault: who resists and what happens?”, American Journal of Public Health, Vol. 79 No. 1, pp. 27-31.
Spitzberg, B.H. and Hoobler, G. (2002), “Cyberstalking and the technologies of interpersonal terrorism”, New Media & Society, Vol. 4 No. 1, pp. 71-92.
Corresponding author
About the authors
Erica R. Fissel is based at the ICF, Tallahassee, Florida, USA.
Jin R. Lee is based at the Department of Criminology, Law and Society, George Mason University, Fairfax, Virginia, USA.