Help-Seeking From Websites and Police in the Aftermath of Technology-Facilitated Victimization

Abstract

This study looked at experiences of help-seeking from websites and police following an episode of technology-facilitated abuse. It used data from a nationally representative online panel of adults aged 18 to 28, sampled from Ipsos Knowledge Panel. A total of 1,952 unique victimization episodes from childhood and adulthood were identified and used in analyses. Participants were asked about whether they experienced 11 different types of technology-facilitated abuse (TFA), whether the incident was reported to the website or police, barriers to reporting, and features of the website’s or law enforcement’s response. Other follow-up information included victim gender, age, relationship to the perpetrator, and negative emotional impact (NEI) associated with the incident. Results found very low rates of reporting to both websites (7.3%) and law enforcement (4.8%). Image-based offenses had higher rates of reporting. A greater NEI significantly increased the odds of reporting to each source. Participants were largely unsatisfied with response from websites and police. Only 42.2% said the website did something helpful and only 29.8% found police helpful. Our findings suggest a need for major improvements in how websites and law enforcement respond to victims of technology-facilitated offenses. They need to have more helpful information and more ways of offering support. Websites need more specifics about the types of violations that warrant reporting, clearer signposts, and encouragement about how to get help and a better publicized commitment to a rapid and serious review. Law enforcement needs more education and training to avoid dismissive and judgmental reactions and to ensure sympathetic and respectful responses.

Keywords

technology-facilitated abuse help-seeking disclosure online victimization

Introduction

Like other social activities, criminal offenses have been migrating to the digital environment. Recent research shows that over 15% of children and youth report technology-facilitated offenses, including unwanted sexual solicitation, non-consensual image sharing and taking, or sextortion (Finkelhor et al., 2022). Concerns have been raised about the harmful features of these digital offenses, including that technology has facilitated more ready access to children by predatory strangers and adults, that it has reduced inhibitions to aggression, and that is has heightened the impact by rendering the aggressions more socially visible, pervasive, and harder to escape from (Underwood & Ehrenreich, 2017). There is limited research investigating these concerns and, in some cases, these claims have been challenged. For example, online peer aggression among youth has not been found to be more frequent or impactful than face-to-face aggression (Mitchell et al., 2016).

A less discussed issue about digital offenses concerns how this new technology environment may impact dynamics and prevalence of reporting and help-seeking behavior (Fissel, 2021; Mumford et al., 2022). On the one hand, technology has features that might facilitate reporting. One is that these offenses leave digital footprints in the form of time-stamped messages, statements, and images that can back up victim claims. Another feature is that the online environment can provide direct and immediate avenues for reporting to platforms, moderators, and help-sources.

On the other hand, there are barriers to reporting in the technology environment. The norms and rules about violations may be less clear. For example, the offenses of physical and sexual assault offline may be more clearly defined than online aggression or the misuse of sexual images. It is also possible that avenues of help-seeking in the digital environment, while available, are not clearly taught, communicated, or recognized. Furthermore, it is possible that online help sources or law enforcement have already developed a poor reputation for being unhelpful and insensitive (Wolak & Finkelhor, 2016). Past research has pointed to reluctancy among victims to turn to police for help because of past experiences with law enforcement during which they did not follow up or take the incident seriously (Garvin & Beloof, 2015; Garvin & LeClair, 2013; Wemmers, 2002, 2013). Similarly, research on online victim populations have catalogued complaints about the poor responses to reports about new digital offenses like sexual extortion (Wolak & Finkelhor, 2016). A better understanding of the frequency of help-seeking among victims of technology-facilitated abuse (TFA), the targets of such efforts, and the factors and contexts that inhibit or facilitate help-seeking is clearly needed.

To address the gaps in research regarding help-seeking and TFA, this study does several things. First, we review the existing literature on help-seeking in the context of off- and online victimization, focusing on dynamics that influence decisions to seek help. In the next section, we use a national sample of TFA victims to estimate prevalence of reporting to police and internet sources and examine several aspects related to victims’ experiences with reporting. We then examine whether there are significant differences in rate of reporting by incident characteristics and which factors most strongly predict likelihood of reporting to police or internet sources. Last, we discuss these findings in the context of the existing literature and provide recommendations for law enforcement and other victim services agencies.

Literature Review

Below, we provide a review of the existing literature on help-seeking following victimization among children and adults. Several factors are likely to influence help-seeking including offense context, emotional impact following the incident, the type of victimization, and individual-level victim characteristics. What is known regarding each of these dynamics, as well as gaps in the research, is highlighted below.

TFA and Help-Seeking

There is limited research on help-seeking and reporting behaviors of victims of TFA. One recent study found that, when presented with various crime and cybercrime scenarios, participants were most likely to identify cybercrimes involving threatened violence as warranting police involvement, while sending sexual comments and posting photos were least likely to be viewed as needing police intervention (Graham et al., 2020). In the same study, however, respondents were less likely to report in-person unwanted sexual comments than online unwanted sexual comments. Furthermore, online unwanted sexual comments were viewed as more likely to be met with police action than the “traditional” or in-person perpetration of the same crime. This suggests that some online offenses may be viewed as more police worthy than those experienced in-person (Graham et al., 2020).

Offense Seriousness

Past research has shown that victimization “seriousness” or severity is associated with reporting and help-seeking in both traditional crime contexts and in victimizations occurring online (Reyns & Englebrecht, 2014). Experiencing more school-, work-, or health-related consequences following victimization (Fissel, 2021; Mumford et al., 2022) or increased feelings of intimidation (Reyns & Englebrecht, 2010) have been shown to increase the likelihood of reporting to police or seeking other informal sources of help. However, less attention has been paid to whether the most emotionally harmful technology-facilitated incidents are being disclosed to law enforcement or support services (Henry et al., 2018). Recent research has highlighted how different TFA incident dynamics are associated with emotional impact (Finkelhor et al., 2023), but its relevance to help-seeking for online offenses remains unclear.

TFA Context and Incident Dynamics

Help-seeking and reporting of online victimization has more often been studied in the specific contexts of stalking and cyberstalking than the wider range of technology-facilitated offenses (Fissel, 2021; Reyns & Englebrecht, 2010). Though recent research using a nationally representative sample of young adults aged 18 to 35 included a broader definition of TFA, including incidents of communication abuse, reputational harm, surveillance, tracking, and financial fraud, the findings do not differentiate between different types of TFA exposure and likelihood of reporting to support services (Mumford et al., 2022). Victims of different forms of TFA may report victimization episodes to websites or police at different rates. For example, research on non-consensual dissemination of sexually explicit media or image-based abuse has shown that victims are hesitant to seek help due to feelings of embarrassment, shame, or guilt associated with taking and sending explicit photos (Campbell et al., 2020).

Research on help-seeking and reporting of technology-facilitated offenses must also consider how the relationship between the victim and perpetrator may play a role in disclosure. Past research found victims of cyberstalking were significantly more likely to report the incident to law enforcement if the perpetrator was a current intimate partner compared to a stranger or non-intimate partner (Fissel, 2021). Victims were also more likely to seek help from professional sources, such as crisis hotlines, counseling, or shelter and safe house services, when the cyberstalker was a current intimate partner compared to other perpetrators (Fissel, 2021).

However, more recent research found the opposite effect, where victims were less likely to seek both technological support and justice or legal assistance when the perpetrator was a current or former intimate partner compared to all other relationship types (Mumford et al., 2022). On the one hand, victims may be more likely to report the incident to the police or website when the perpetrator is an intimate partner who may also present a threat in-person. On the other hand, victims of abuse often feel loyal to intimate partners and resist seeking help at the risk of harming their relationship or partner’s reputation. Additional research on the relationship between perpetrator identity and help-seeking or reporting is needed to help inform abuse prevention and victim support efforts.

Victim Characteristics

As with traditional crime, there are gender differences in help-seeking following online victimization. Gender may be associated with whether technology-based offenses are viewed by victims as criminal or police worthy. Recent work examining perceptions of cyberstalking found that women were more likely than men to view cyberstalking as violating the law (Ahlgrim & Terrance, 2018). Gender norms likely play a significant role in shaping help-seeking behaviors, as well as propensity to turn to law enforcement (Hullenaar & Ruback, 2021). Where girls and women are socialized to accept help from others, boys and men are socialized to be self-reliant, independent problem-solvers (Eagly & Crowley, 1986; Himmelstein & Sanchez, 2016; Prentice & Carranza, 2002). So, it is possible that women are more likely to turn to informal or formal sources of support, such as law enforcement, while male victims will attempt to handle the problem on their own by changing their online behaviors or reporting the incident to the website—a help-seeking method that maintains some anonymity.

Age at victimization may also influence likelihood of reporting to a website or the police. Offenses against children and youth are generally found to be underreported (Finkelhor & Wolak, 2003; Finkelhor et al., 2001). Victimizations against children are less likely to be perceived by the victim as serious or criminal offenses (Finkelhor et al., 2001). Young people are also not confident about their authority and credibility. Moreover, parents and caregivers interpose another layer of possible obstacles and complexities to the process. For example, parents may choose to investigate and handle the situation on their own, especially in cases of sibling or peer aggression (Finkelhor et al., 2001). Often, studies are limited to juvenile- or adult-only samples. How age at victimization may impact help-seeking or reporting behaviors in the context of technology-facilitated offenses remains underaddressed.

Study Aims

This study aims to describe the experiences of a national sample of victims with options for seeking help in the wake of technology-related offenses. It focuses on help from both the technology platforms that are the context for the offense (i.e., the website or social media application) as well as law enforcement and examines how both incident and victim characteristics influence likelihood of reporting the offense. Additionally, this study highlights the outcome of the help-seeking as well as reasons for not reporting the offense.

Methods

The study was conducted in the United States using the nationally representative Ipsos online KnowledgePanel (KP). KP is a sample that Ipsos has recruited via Address Based Sampling, from mail addresses gleaned from national universal address data bases. After the mail recruitment, participants agreed to participate in regular online surveys. Digital devices were provided to any recruited sample member who lacked devices to participate. The KP panelists who were 18 to 28 years old (13,884) were solicited for the current survey. This age range was chosen to capture the experiences of young adults as well as allow for retrospective reporting of incidents that occurred during childhood. In total, 2,639 panel members participated in the survey by the end of data collection, with an overall participation rate of 20%. The study was approved and overseen by the Human Subjects Review Board of the University of New Hampshire.

Of the 2,639 completed surveys, 1,215 endorsed one or more of the screening questions about possible online victimizations. The final weighted sample had a slightly higher proportion of females and older young adults than the U.S. population of 18-to-28 year-olds. Weights were developed for the sample that adjust for non-response and the prioritization of lower base-rate incidents among those with multiple exposures.

To assess rates of reporting across all incidents, an episode-level file was created where each victimization incident was recoded as a separate observation. The episode-level sample consisted of 3,127 separate lifetime victimization incidents. For those with multiple victimizations, the survey gathered follow-up information on two, prioritizing for episodes that were of less frequent occurrence in the sample overall, as determined by a survey pretest. Analyses for the current research were conducted on the subsample of 1,975 incidents for which full follow-up information was obtained. After screening out cases which did not qualify for TFA, the final sample used in analyses was 1,952.

The final sample was 66.5% (95% confidence interval [CI]: [62.6, 70.3]) female, 29.8% [26.1, 33.7] male, and 3.7% [2.5, 5.5] other gender. Incident victims were 53.0% [49.3, 56.8] non-Hispanic White, 24.0% [21.0, 27.3] Hispanic, 12.5% [10.0, 15.6] non-Hispanic Black, 6.7% [5.1, 8.9] two or more races, and 3.7% [2.8, 4.9] some other race. The mean age of respondents was approximately 23 years [23.2, 23.7]. Respondents in the sample were most likely to have some college education (35.7% [32.3, 39.2]), followed by a high school diploma (30.5% [26.7, 34.6]), a Bachelor’s degree or higher (25.9% [23.5, 28.5]), and less than a high school education (7.9% [5.7, 10.8]).

Independent Variables

The offense episode types included the following;

Non-consensual image sharing

This item was measured using the following survey question: “Has someone ever shared with other people a sexual picture or video of you without your permission?.”

Non-consensual image taking

Non-consensual taking was designed to measure both the taking of sexual images and making, through photoshop or photo editing, of sexual images of the victim without consent. The survey item asked, “Has someone ever taken or made a sexual picture or video of you without your permission?.”

Forced image recruitment

This item asked, “Has someone ever threatened, tried to force you, or strongly pressured you to provide sexual pictures or videos online or through a cell phone?.” Incidents were counted as forced image recruitment whether or not the perpetrator followed through with the threat and with or without and image being provided.

Threatened sharing

“Has someone ever threatened to share a sexual picture or video of you to get you to do something—like take or send other sexual pictures of yourself, have a sexual relationship with them, pay them money, or something else?.” Incidents were considered threatened sharing whether or not the threat was followed through.

Cyberstalking

Cyberstalking was measured through the following question: “Has someone ever repeatedly contacted you online, on the phone, or in person when you did not want it, in a way that made you very afraid, anxious, or angry?.” Those respondents who reported that the unwanted contact was in-person only were removed from analyses (n = 23).

Unwanted contact

Unwanted contact was measured using 3 items in the survey: unwanted sexual talk (“Before the age of 18, did anyone ever use the internet or a cell phone to try to get you to talk about sex when you did not want to?”), unwanted sexual questions (“Before the age of 18, did anyone ever use the internet or a cell phone to ask you for sexual information about yourself when you did not want to answer those questions? This means very personal questions, like what your body looks like or sexual things you have done.”), and unwanted sexual acts (“Before the age of 18, did anyone ever use the internet or a cell phone to ask you to do something sexual that you did not want to do?”). Respondents were considered as experiencing unwanted contact if they reported any one of these 3 items.

Older partner voluntary

This item was designed to measure sexual relationships between a juvenile and older partner. The question wording was as follows: “Before the age of 18, did you have intimate sexual conversations or share sexual pictures or videos (online or through a cell phone), even if you wanted to, with a person who was 5 or more years older than you?.”

Commercial sexual talk, images, or other

Commercial sexual activity was measured using 3 items in the survey. Respondents were asked, “Have you done any of the following things over the internet or a cell phone (including texting) in exchange for money, drugs, or other valuable items?: (a) sexual talk; (b) making, sending, or posting sexual pictures or videos of yourself; or (c) any other sexual activity?” Incidents were coded as commercial sexual activity if they endorsed any of the 3 items.

Respondent demographics

Demographic information of respondents was collected through panel data and survey items, including age at victimization and gender.

Relationship to perpetrator

Relationship to perpetrator was categorized as either intimate partner, friend or relative, other acquaintance, someone known only online, or unknown.

Negative emotional impact (NEI)

Respondents were asked to rate how much they felt, at the time of victimization, each of the following on a scale of “Not at all” to “Extremely”: (1) “Angry,” (2) “Afraid,” (3) “Sad,” (4) “Embarrassed,” (5) “Anxious or Worried,” (6) “Like you couldn’t trust people?,” (7) “Like you were alone?,” and (8) “Ashamed.” The NEI items were strongly interrelated. In a principal component factor analysis, all items loaded in the .79 to .82 range on a single factor except for anger that loaded .68. That factor score was used in the assessment of NEI.

Dependent Variables

Reporting to website or app

Following each set of screener follow-up questions, respondents were asked, “Did you or someone else make a report or complaint about what happened to any website or app?.” This information was collected for each of the 1,952 incidents. Those who answered yes were asked whether the website was helpful (“Did this (first) app or website do something helpful in response to your report (for example, removing an image or suspending an account?”), unhelpful (“Was this (first) app or website unhelpful in any way, for example by refusing to help or ignore the report?”), and asked about ease of reporting (“How easy was it to understand how to make a report or complaint at this website or app?”). Respondents were also asked whether the report ended up helping the situation and any reasons for not reporting the situation to a website or app.

Reporting to police

Respondents were also asked, “Did you or someone else report this situation to the police or did the police find out by some other way?.” This information was collected for each of the 1,952 incidents. Those who answered yes were asked whether the police were helpful (“Did the police do something helpful in response to your report (e.g., removing an image or suspending an account)?”), unhelpful (“Were the police unhelpful in any way, for example by refusing to help or ignoring the report?”), and asked to provide more information on what was helpful or unhelpful.

Analytic Plan

Data were analyzed in Stata/SE Version 17.0. Survey weights were used in univariate and bivariate analyses. Chi-square (χ²) tests were used to compare rates of reporting by incident and victim characteristics. Due to the small cell sizes, survey weights were not used in multivariate logistic regressions.

Results

Of the 1,952 TFA incidents with full follow-up information, cyberstalking represented the largest proportion (20.0% [16.9, 23.3]), followed by unwanted contact (19.3% [16.4, 22.5]), forced image recruitment (13.4% [11.2, 15.8]), and non-consensual image sharing (12.9% [10.5, 15.6]). Threatened sharing and older partner voluntary incidents each accounted for approximately 10% of incidents, while non-consensual image taking and commercial sexual activity represented 7.4% [5.8, 9.4] and 6.7% [5.2, 8.5] of incidents, respectively.

Reporting to Website or App

Overall, rates of reporting were low (Table 1). Across all incidents, 7.3% [5.6, 9.4] were reported to a website or app. Among those incidents that were reported, 42.2% [29.6, 56.0] were met with a helpful response, while 26.6% [17.1, 37.8] were perceived as unhelpful. The majority of respondents reported that it was somewhat, very, or extremely easy to make a report on the website or app (83.9% [72.8, 91.1]). Reporting the incident to the website or app ended up helping the situation in less than one-third of cases that were reported (29.2% [19.3, 41.7]).

Table 1.

Rates of Reporting to Website or App Among All Incidents With Follow-Up Information (n = 1,952).

	Weighted % [95% CI]
Make report to website or app? (% yes) (n = 127)	7.3 [5.6, 9.4]
If yes, website helpful response? (% yes) (n = 47)	42.2 [29.6, 56.0]
If yes, website unhelpful response? (% yes) (n = 30)	26.6 [17.1, 38.7]
If yes, how easy was it to make report on website? (n = 120)
Not easy (n = 12)	7.9 [4.0, 15.0]
Somewhat easy (n = 37)	31.6 [20.7, 44.9]
Very easy (n = 36)	34.3 [21.7, 49.6]
Extremely easy (n = 23)	18.0 [10.9, 28.5]
Don’t know/Not sure (n=12)	8.2 [3.1, 19.9]
If yes, did report end up helping situation? (% yes) (n = 50)	29.2 [19.3, 41.7]
Do any of the following reasons describe why you did not report to website? (n = 1,807)
Fear/embarrassment (n = 657)	35.4 [31.8, 39.3]
Low intensity (n = 1,028)	54.7 [50.7, 58.6]
Did not think it would help (n = 568)	29.6 [26.3, 33.3]
Couldn’t figure out/nowhere to report (n = 292)	13.1 [10.8, 15.7]
Some other reason, Don’t know or prefer not to say (n = 351)	21.1 [18.0, 24.6]

Note. CI = confidence interval.

Among those who did not report the incident to the website or app, common reasons for not reporting included that the episode was of low intensity (54.7% [50.7, 58.6]), being fearful or embarrassed (35.4 [31.8, 39.3]), or thinking it would not help (29.6% [26.3, 33.3]). Fewer respondents said they did not report the incident because they couldn’t figure out how or that there was nowhere to report (13.1% [10.8, 15.7]). Approximately one-fifth of respondents indicated some other reason for not reporting, didn’t know, or preferred not to say.

Rates of reporting to the website or app were significantly associated with type of victimization (Table 3). Threatened sharing incidents were most likely to be reported to a website or app (15.3% [7.8, 27.7]), followed by non-consensual image sharing (12.2% [7.4, 19.6]) and non-consensual image taking (10.8% [5.9, 18.9]). Commercial sexual activity and older partner voluntary episodes had the lowest rates of reporting, at 1.6% [0.3, 8.6] and 1.1% [0.2, 7.3], respectively.

Males (6.4% [3.6, 11.1]) and females (7.3% [5.4, 9.9]) had similar rates of reporting to the website or app. Perpetrator relationship was not significantly associated with reporting to the website or app in bivariate analyses.

Reporting to Law Enforcement

Rates of reporting the incident to police were even lower than to websites, with only 4.8% [3.3, 6.9] of episodes disclosed to the police (Table 2). Of those who reported to police, 29.8% [17.8, 45.6] said the police did something helpful in response to the situation while 48.5% [30.7, 66.6] said the police were unhelpful.

Table 2.

Rates of Reporting to Police Among All Incidents With Follow-Up Information (n = 1,952).

	Weighted % [95% CI]
Report to police? (% yes) (n = 81)	4.8 [3.3, 6.9]
If yes, police helpful response? (% yes) (n = 35)	29.8 [17.8, 45.6]
If yes, what was helpful?
Were sympathetic (n = 20)	53.7 [30.8, 75.1]
Conducted an investigation (n = 19)	72.7 [52.7, 86.5]
Gave you resources (n = 9)	26.7 [12.3, 48.7]
Contacted person responsible (n = 16)	35.8 [18.2, 58.3]
Something else (n = 2)	0.9 [0.2, 3.8]
Don’t know/Not sure (n = 1)	0.8 [0.1, 5.4]
Where the police unhelpful in any way? (% yes) (n = 28)	48.5 [30.7, 66.6]
If yes, what was unhelpful?
I felt blamed (n = 15)	24.9 [10.5, 48.4]
I felt situation wasn’t taken seriously (n = 22)	54.4 [24.8, 81.2]
Police did not understand (n = 9)	19.4 [7.4, 42.2]
Police said they could not do anything (n = 17)	83.6 [58.3, 94.9]
Something else (n = 3)	3.6 [0.8, 14.7]

Note. CI = confidence interval.

When asked what the police did that was helpful, respondents were most likely to cite that the police conducted an investigation (72.7% [52.7, 86.5]). Responses also included the police were sympathetic (53.7% [30.8, 75.1]), contacted the person responsible (35.8% [18.2, 58.3]), and provided resources (26.7% [12.3, 48.7]). Less than 1% each cited some other reason or responded don’t know/not sure.

Among those who said the police were unhelpful, respondents were most likely to say that the police said they could not do anything (83.6% [58.3, 94.9]) as the reason for being unhelpful, followed by the situation not being taken seriously (54.4% [24.8, 81.2]), feeling blamed (24.9% [10.5, 48.4]), or that the police did not understand (19.4% [7.4, 42.2]). Fewer respondents said some other reason (3.6% [0.8, 14.7]).

Screener type was significantly associated with reporting to police (Table 3). Episodes of threatened sharing had the highest rates of reporting (14.6% [6.8, 28.8]). Other incident types had lower rates of reporting. Victim gender and relationship to perpetrator were not significantly associated with reporting to police in bivariate analyses.

Table 3.

Rates of Reporting by Screener Type, Gender, and Victim Relationship With Perpetrator With Chi-Square (χ²) Tests (n = 1,952).

	Report to Website or App(n = 127)		Report to Police(n = 81)
	Weighted % of Each Category [95% CI] (n)	p-Value	Weighted % of Each Category [95% CI] (n)	p-Value
Screener type		.0009 (***)		.0001 (***)
Non-consensual image sharing (n = 222)	12.2 [7.4, 19.6] (26)		6.3 [2.9, 13.2] (12)
Non-consensual image taking (n = 171)	10.8 [5.9, 18.9] (17)		9.4 [4.6, 18.0] (12)
Forced image recruitment (n = 320)	3.1 [1.5, 6.5] (11)		1.4 [0.4, 4.7] (4)
Threatened sharing (n = 161)	15.3 [7.8, 27.7] (16)		14.6 [6.8, 28.8] (14)
Cyberstalking (n = 359)	9.1 [5.6, 14.4] (36)		5.3 [2.3, 11.9] (26)
Unwanted contact (n = 381)	4.4 [1.7, 11.1] (17)		1.0 [0.4, 3.0] (5)
Older partner voluntary (n = 194)	1.1 [0.2, 7.3] (1)		1.9 [0.6, 5.8] (4)
Commercial sexual activity (n = 138)	1.6 [0.3, 8.6] (2)		1.7 [0.5, 6.2] (3)
Gender		.4315		.4424
Male (n = 301)	6.4 [3.6, 11.1] (24)		4.0 [1.8, 8.6] (15)
Female (n = 1576)	7.3 [5.4, 9.9] (93)		5.4 [3.6, 8.1] (66)
Other (n = 75)	13.1 [5.3, 29.1] (10)		(0)
Relationship to perpetrator		.3731		.1107
Intimate partner (n = 449)	8.5 [5.2, 13.6] (29)		8.0 [4.4, 14.1] (27)
Friend/relative (n = 269)	6.8 [3.3, 13.6] (15)		1.5 [0.7, 3.3] (10)
Other acquaintance (n = 495)	3.8 [2.0, 7.3] (19)		4.8 [2.7, 8.6] (21)
Online (n = 137)	5.8 [3.1, 10.6] (14)		1.8 [0.6, 5.6] (3)
Unknown (n = 276)	7.6 [3.9, 14.5] (21)		3.3 [0.8, 11.9] (5)

Note. CI = confidence interval.

***

p < .001.

Multivariate Analyses

Table 4 shows the results of the logistic regressions of reporting to the website or app (Models 1 and 2) and reporting to police (Models 3 and 4) on incident and victim characteristics and negative emotional impact (NEI) score. As seen in Model 1, using incidents of unwanted contact as the reference category, episodes involving non-consensual image sharing increased the odds of reporting to a website or app by two-fold (odds ratio [OR] 2.2 [1.0, 4.7], p < .05), while those involving older partner voluntary exchanges had one-tenth the odds of reporting (OR 0.1 [0.0, 1.0], p < .05). Other incident types were not significantly associated with odds of reporting to a website or app when compared to episodes of unwanted contact. Age at the time of incident, victim gender, and the victim’s relationship to the perpetrator also did not significantly predict reporting to a website or app in the first model. NEI score was added into the model in Model 2. A high level of NEI was significantly associated with an increased odds of reporting to the website or app (OR 1.4 [1.1, 1.8], p < .05). After adjusting for NEI, non-consensual image sharing and older partner voluntary incident type were no longer significant.

Table 4.

Logistic Regression Predicting Reporting to Website/App (Models 1 and 2) and Reporting to Police (Models 3 and 4).

	Report to Website or App(n = 1,623)		Report to Police(n = 1,571)
	OR [95% CI]^a
Screener type (ref = unwanted contact)	Model 1	Model 2	Model 3	Model 4
Non-consensual image sharing	2.2* [1.0, 4.7]	1.6 [0.7, 3.6]	4.0* [1.0, 15.4]	2.3 [0.6, 9.1]
Non-consensual image taking	1.7 [0.7, 4.0]	1.2 [0.5, 3.1]	4.9* [1.2, 19.4]	2.9 [0.7, 11.9]
Forced image recruitment	0.7 [0.3, 1.7]	0.6 [0.3, 1.6]	1.0 [0.2, 5.1]	0.8 [0.2, 4.2]
Threatened image sharing	1.8 [0.7, 4.2]	1.2 [0.5, 3.0]	8.3** [2.2, 31.2]	4.3* [1.1, 17.0]
Cyberstalking	2.0 [0.9, 4.1]	1.9 [0.9, 3.9]	8.2** [2.3, 28.7]	8.0** [2.3, 28.2]
Older partner voluntary	0.1* [0.0, 1.0]	0.1 [0.0, 1.0]	2.0 [0.4, 10.0]	2.2 [0.4, 11.3]
Commercial sexual activity	0.3 [0.1, 1.6]	0.3 [0.1, 1.5]	2.6 [0.5, 13.6]	2.4 [0.5, 12.7]
Age at incident (ref = 17 or younger)
18 or older	1.2 [0.8, 1.9]	1.3 [0.8, 2.0]	1.2 [0.7, 2.0]	1.3 [0.8, 2.2]
Victim gender (ref = male)
Female	0.8 [0.4, 1.3]	0.7 [0.4, 1.2]	1.1 [0.5, 2.4]	0.9 [0.4, 2.0]
Other	2.0 [0.8, 5.1]	1.7 [0.7, 4.4]	-empty-	-empty-
Relationship to perpetrator (ref = intimate partner)
Friend/relative	0.8 [0.4, 1.6]	0.9 [0.5, 1.7]	0.6 [0.3, 1.3]	0.6 [0.3, 1.4]
Other acquaintance	0.6 [0.3, 1.1]	0.6 [0.3, 1.2]	0.7 [0.4, 1.4]	0.8 [0.4, 1.5]
Online	1.7 [0.9, 3.5]	1.7 [0.9, 3.6]	0.4 [0.1, 1.2]	0.4 [0.1, 1.3]
Unknown	1.3 [0.7, 2.3]	1.3 [0.7, 2.5]	0.3* [0.1, 0.9]	0.3* [0.1, 1.0]
Negative emotional response		1.4* [1.1, 1.8]		1.8** [1.3, 2.5]

Note. OR = odds ratio; CI = confidence interval.

Due to rounding, 95% CI that include 1.0 may also appear as statistically significant.

p < .05. **p < .01.

In Model 3, several incident types were significantly associated with odds of reporting to the police when compared to episodes of unwanted contact. Non-consensual image sharing (OR 4.0 [1.0, 15.4], p < .05), NCT (OR 4.9 [1.2, 19.4], p < .05), threatened image sharing (OR 8.3 [2.2, 31.2], p < .01), and cyberstalking (OR 8.2 [2.3, 28.7], p < .01) significantly increased the odds of reporting to police between four- and eight-fold.

Respondents who did not know the perpetrator had a significantly lower odds (OR 0.3 [0.1, 0.9], p < .05) of reporting the incident to police when compared to those incidents that occurred with an intimate partner. None of the other victim or incident characteristics significantly influenced odds of reporting to police.

After including NEI in Model 4, non-consensual image sharing and non-consensual image taking were no longer significant. A higher level of NEI score was associated with an increased odds of reporting to police (OR 1.8 [1.3, 2.5], p < .01). Threatened image sharing and cyberstalking incidents remained strong predictors of reporting to police after adjusting for NEI score.

Discussion

Overall, rates of reporting to both websites/apps and police were low. Less than one-tenth of those who experienced technology-facilitated online victimization reported the incident to the website or app on which the victimization occurred and even fewer reported the offense to law enforcement. These findings are consistent with past research that finds low rates of reporting of both off- and online victimization incidents (Finkelhor et al., 2011).

Among children and youth victims, offenses may often be viewed by the victim or the victim’s family as non-criminal, and therefore not warranting help-seeking. Offenses against children are generally found to be underreported to law enforcement agencies (Finkelhor & Wolak, 2003; Finkelhor et al., 2001) and barriers to reporting—such as powerlessness or feeling unsure about their ability or credibility to report incidents of victimization—may be more common among child victims. However, our study found no significant effect of age on rates of reporting; youth and adult victims had similar odds of reporting their victimization episode to a website or app or to police. While past research has found significantly higher rates of reporting of online sextortion episodes to a website or app among adult victims when compared to minors (Wolak et al., 2018), less attention has been paid to police response to adult victims of TFA (Powell & Henry, 2018) or differences in rates of help-seeking between youth and adult victims.

Our findings may suggest that increased access to the internet and online environment among youth and teens, and therefore increased exposure to potential online victimization, is diminishing the difference between how youth and adult victims view, process, and seek help for online victimization. In a recent study among victims under age 18, there was no significant difference between age cohorts with respect to NEI of technology-facilitated victimization (Finkelhor et al., 2023). Furthermore, whether the perpetrator was a peer or adult did not influence emotional impact among youth. Though it is unclear whether there are significant differences between youth and adults in emotional impact following victimization, it is possible that similar levels of NEI could result in similar rates of reporting episodes between youth and adults.

In fact, past research has highlighted how episode intensity, “seriousness,” and consequences following victimization are associated with reporting and help-seeking for both in-person as well as online victimization (Fissel, 2021; Mumford et al. 2022; Reyns & Englebrecht, 2014). Consistent with past findings, the present study found that higher NEI score was positively and significantly associated with odds of reporting to both websites and police, after controlling for victim and episode dynamics. Episodes that result in more negative emotions are likely viewed as more harmful and, perhaps, more criminal in nature by victims. This finding means that, though overall rates remain low, victims are seeking help for some of the most emotionally upsetting victimization experiences. It is important for websites and law enforcement to acknowledge that incidents being reported may be particularly traumatic and therefore provide victims with appropriate resources, such as information on mental health support, in addition to information on internet safety or legal resources.

The type of technology-facilitated victimization was also significantly associated with rates of reporting. NCS incidents increased the odds of reporting the victimization to a website or app when controlling for victim and incident characteristics, while experiencing episodes of older partner voluntary exchanges significantly decreased the odds of reporting. In addition to cyberstalking episodes, several types of image-based victimization also significantly increased the odds of reporting to police. Our study adds to the literature on help-seeking among TFA victims by examining the association between each type of victimization and rates of reporting, rather than limiting the sample to victims of one type of online offense such as cyberstalking.

The present study found higher rates of reporting among victims experiencing image-based episodes. Image-based abuse is considered an especially harmful, pernicious, form of abuse that can have lasting impacts (McGlynn et al., 2021; Rackley et al., 2021). A reason for heightened reporting may be that victims of image-based offenses are looking to websites and/or police to remove the offensive image from circulation to prevent further harm. However, reasons that image-based abuse victims do report incidents to the police remains understudied.

Victims have identified several barriers to reporting offenses to websites. While participants found it easy to make a report on a website or app, less than half were met with a helpful response and even fewer believed the report ended up helping their situation. In addition, many victims did not report the incident to the website because it did not occur to them, they felt it was not a big deal, or the situation stopped without help. Victims also cited fear or embarrassment and that they did not think it would help as barriers to reporting. Fear is a common barrier to reporting off- and online crime (Ranapurwala et al., 2016). In the present study, fear was measured as being fearful that the report would not be anonymous, that the person who threatened you would find out, or that you would be in trouble with or judged by family.

To alleviate these barriers, websites and social media applications can take additional steps to promote reporting and help-seeking. They can clearly define and give examples of violations. They can post more frequent messages of encouragement to seek help. They can counter most common inhibitors, for example, by framing reporting as a way of protecting others and the community of users. They can commit publicly to rapid and sympathetic response along with details of typical positive outcomes. They can ensure anonymity and confidentiality in the process. They can also do a better job of evaluating and publishing about the effectiveness of help promotion systems. A 2016 study on the effectiveness of the self-regulatory systems of social media companies to prevent cyberbullying among children found that, while companies such as Facebook, Instagram, or YouTube had clearer definitions of cyberbullying, “less established” social media companies were less clear on their definitions of online harassment (Milosevic, 2016, p. 5171). Transparency by websites and social media applications in what defines TFA, how reports are managed, and the outcomes of these reports may help alleviate fear and hesitancy among by victims in self-reporting victimization incidents.

Victims of TFAs were unsatisfied with police response, as well. Among those who reported the incident to police, less than one-third found the response helpful, while nearly half stated that the police were unhelpful. A large percentage of victims who reported that the police were unhelpful also reported that the police said they could not do anything, and approximately half felt that the situation wasn’t taken seriously. This is a serious indictment of law enforcement and is consistent with other research (Henry et al., 2020). It suggests a large gap in training and failure to establish helpful response systems.

The findings about high levels of dissatisfaction with police response likely reflect a number of realities. As a relatively new crime domain, many police first responders may have had little training on how to handle these offenses (Setter et al., 2021). They may not be aware of what the legal options are. They may be unsure of their jurisdiction if the perpetrator is unknown or from somewhere else, or they may doubt the likelihood of having success in an investigation and, particularly in the case of juvenile victims or offenders, see this as a less serious crime (Wolak et al., 2018).

Police could benefit from more education about the nature, dynamics, and the variety of helpful responses to image-based abuse (NCMEC, 2020). There are specialized internet crime investigation task forces across the country, but these resources are severely taxed. It may be useful for local agencies to have more specialists who can be tasked to respond to these offenses. For juvenile victims, it may be valuable to make referrals to local children’s advocacy centers—victim support agencies that exist in over 1,000 localities (National Children’s Advocacy Center, 2021). Of course, law enforcement will not be able to investigate all complaints brought to their attention but having positive ways of supporting and providing information or other resources to victims could be helpful in improving complainant satisfaction.

Among study participants who did report that police response was helpful, they were most likely to state that the police conducted an investigation, and much less likely to state that the police gave them resources, when asked what the police did that was helpful. Victims may first seek help from other sources and turn to law enforcement when they believe the incident to be police-worthy when, for example, the incident has increased in intensity or duration. In fact, length of victimization of 1 month to 1 year, compared to less than 1 week, significantly increased the odds of seeking help from law enforcement—but not informal sources of support—among a sample of cyberstalking victims (Fissel, 2021). These findings on helpful police responses, taken together with findings on unhelpful police responses, further highlight the need for law enforcement to have clear protocol, training, and improved education in acknowledging, investigating, and following-up with victims of technology-facilitated offenses to provide victims with the support they need.

Beyond reporting, there have been several calls to action to address TFA more broadly. A 2020 collaborative report by the Priority Criminal Justice Needs Initiative, a project of RAND Corporation, the Police Executive Research Forum, RTI International, and the University of Denver, highlighted priority needs in addressing TFA. Several such needs were identified, including the implementation of public education and TFA prevention efforts, prioritizing awareness of TFA among criminal justice practitioners, and improving criminal justice practices and policies for addressing TFA (Witwer et al., 2020). Moreover, in June 2020, the Biden Administration established a White House Task Force on Online Harassment and Abuse, calling out, among other forms of abuse, the non-consensual distribution of intimate digital images (The White House, 2022). Such recommendations included improving coordination among departments, agencies, and offices to maximize effectiveness in combatting technology-facilitated gender-based violence; enhancing and expanding data collection and research efforts to measure cost, prevalence, exposure, and impact of technology-facilitated violence; increasing access to survivor-centered services, information, and victim support; and increasing training and technical assistance for criminal justice organizations (The White House, 2022). These recommendations are consistent with our findings, addressing a gap in current law enforcement education as well as insufficient transparency and/or data sharing by social media websites. Additionally, these reports support our calls for additional research in this area to better inform policy and training programs for sources of formal support and victim services.

Limitations

While this study adds to the literature on help-seeking among victims of TFA, there are some limitations. Though we ask victims about their experiences seeking help from formal sources, the technology platform and law enforcement, we do not measure informal help-seeking measures such as friends or family. Some research indicates that victims may be more likely to turn to informal sources of support rather than law enforcement or professional help (Fissel, 2021), and that there are significant sociodemographic predictors of informal help-seeking (Reyns & Englebrecht, 2014). Future research should consider informal help-seeking in the wake of technology-facilitated victimization and the factors that influence informal help-seeking decisions.

Second, because the rates of reporting to websites or police were so low, small cell sizes precluded us from testing for any moderating effects such as whether TFA type impacts the relationship between NEI score and help-seeking. In past research, image-based victimizations were associated with an increase in NEI (Finkelhor et al., 2023). It is possible that NEI more strongly effects the odds of help-seeking for different types of victimization, such as image-based offenses. Small cell sizes also prevented us from including race and ethnicity as a covariate in analyses or testing for interactions between demographic categories (i.e., age and gender). This is a limitation of this study, and future research using larger samples should seek to examine diversity in help-seeking experiences following technology-facilitated victimization.

Conclusion

This study found low prevalence rates of victims reporting TFA incidents to either websites or law enforcement. Additionally, victims cited several barriers to reporting incidents to websites and high levels of dissatisfaction with police response. To alleviate some of these concerns, websites and law enforcement can take steps to improve the experience of TFA victims. Websites and applications can be more transparent in how they ensure anonymity, how reports are managed, and publicly share data on outcomes of reports. Law enforcement agencies can improve education on the nature, dynamics, and types of responses that victims deem helpful for incidents of technology-facilitated offenses. As digital spaces continue to grow, it is important to consider how and when victims decide to report these offenses to both formal and informal sources of help and the outcomes of help-seeking.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research and/or authorship of this article: This research was funded by grant 2020R2CX0015 from the National Institute of Justice (Drs. Finkelhor and Turner).

ORCID iDs

Deirdre A. Colburn

David Finkelhor

Heather A. Turner

Author Biographies

Deirdre A. Colburn is a PhD candidate in Sociology at the University of New Hampshire specializing in Health and Illness. Her research interests include social determinants of health, mental health, health care utilization, health policy, and help-seeking. Her current research focuses on demographic trends and policy effects surrounding telehealth utilization during the Covid-19 pandemic.

David Finkelhor is the director of crimes against Children Research Center, and Professor of Sociology at the University of New Hampshire. He has been studying the problems of child victimization, child maltreatment, and family violence since 1977. He is well known for his conceptual and empirical work on the problem of child sexual abuse. He has also written about child homicide, missing and abducted children, and internet crimes against children.

Heather A. Turner is a professor of Sociology and Senior Research Associate at the Crimes against Children Research Center at the University of New Hampshire. Her research program has concentrated on social stress processes and mental health, including the effects of violence, victimization, and other forms of adversity on the social and psychological development of children and adolescents. She has over 20 years of research experience on childhood exposure to violence, and has conducted numerous national surveys, and published over 150 articles.

References

Ahlgrim

Terrance

(2018). Perceptions of cyberstalking: Impact of perpetrator gender and cyberstalker/victim relationship. Journal of Interpersonal Violence, 36(7–8), NP4074–NP4093. https://doi.org/10.1177/0886260518784590

Campbell

J. K.

Poage

S. M.

Godley

Rothman

E. F.

(2020). Social anxiety as a consequence of non-consensually disseminated sexually explicit media victimization. Journal of Interpersonal Violence, 37(9–10), NP7268–NP7288. https://doi.org/10.1177/0886260520967150

Eagly

A. H.

Crowley

(1986). Gender and helping behavior: A meta-analytic review of the social psychological literature. Psychological Bulletin, 100(3), 283–308. https://doi.org/10.1037/0033-2909.100.3.283

Finkelhor

Ormrod

Turner

Hamby

(2011). School, police, and medical authority involvement with children who have experienced victimization. Archives of Pediatrics & Adolescent Medicine, 165(1), 9–15. https://doi.org/10.1001/archpediatrics.2010.240

Finkelhor

Turner

Colburn

(2022). Prevalence of online sexual offenses against children in the US. JAMA Network Open, 5(10), e2234471. https://doi.org/10.1001/jamanetworkopen.2022.34471

Finkelhor

Turner

Colburn

(2023). Which dynamics make online child sexual abuse and cyberstalking more emotionally impactful: Perpetrator identity and images? Child Abuse & Neglect, 137, 106020. https://doi.org/10.1016/j.chiabu.2023.106020

Finkelhor

Wolak

(2003). Reporting assaults against juveniles to the police: Barriers and catalysts. Journal of Interpersonal Violence, 18(2), 103–128. https://doi.org/10.1177/0886260502238730

Finkelhor

Wolak

Berliner

(2001). Police reporting and professional help seeking for child crime victims: A review. Child Maltreatment, 6(1), 17–30. https://doi.org/10.1177/1077559501006001002

Fissel

E. R.

(2021). The reporting and help-seeking behaviors of cyberstalking victims. Journal of Interpersonal Violence, 36(11–12), 5075–5100. https://doi.org/10.1177/0886260518801942

10.

Garvin

Beloof

D. E.

(2015). Crime victim agency: Independent lawyers for sexual assault victims. Ohio State Journal of Criminal Law, 13, 67.

11.

Garvin

LeClair

(2013). Polyvictims: Victims’ rights enforcement as a tool to mitigate “Secondary Victimization” in the criminal justice system. National Crime Victim Law Institute. https://www.ojp.gov/ncjrs/virtual-library/abstracts/polyvictims-victims-rights-enforcement-tool-mitigate-secondary

12.

Graham

Kulig

T. C.

Cullen

F. T.

(2020). Willingness to report crime to the police: Traditional crime, cybercrime, and procedural justice special issue: Policing cybercrime. Policing: An International Journal of Police Strategies and Management, 43(1), 1–16.

13.

Henry

Flynn

Powell

(2018). Policing image-based sexual abuse: Stakeholder perspectives. Police Practice and Research: An International Journal, 19(6), 565–581. https://doi.org/10.1080/15614263.2018.1507892

14.

Henry

Flynn

Powell

(2020). Technology-facilitated domestic and sexual violence: A review. Violence Against Women, 26(15–16), 1828–1854. https://doi.org/10.1177/1077801219875821

15.

Himmelstein

M. S.

Sanchez

D. T.

(2016). Masculinity impediments: Internalized masculinity contributes to healthcare avoidance in men and women. Journal of Health Psychology, 21(7), 1283–1292. https://doi.org/10.1177/1359105314551623

16.

Hullenaar

K. L.

Ruback

R. B.

(2021). Gender interaction effects on reporting assaults to the police. Journal of Interpersonal Violence, 36(23–24), NP12997–NP13027. https://doi.org/10.1177/0886260520903134

17.

McGlynn

Johnson

Rackley

Henry

Gavey

Flynn

Powell

(2021). ‘It’s Torture for the Soul’: The harms of image-based sexual abuse. Social & Legal Studies, 30(4), 541–562. https://doi.org/10.1177/0964663920947791

18.

Milosevic

(2016). Social media companies’ cyberbullying policies. International Journal of Communication, 10(2016), 5164–5185.

19.

Mitchell

K. J.

Jones

L. M.

Turner

H. A.

Shattuck

Wolak

(2016). The role of technology in peer harassment: Does it amplify harm for youth? Psychology of Violence, 6(2), 193–204. https://doi.org/10.1037/a0039317

20.

Mumford

E. A.

Rothman

E. F.

Maitra

Sheridan-Johnson

(2022). U.S. young adults’ professional help-seeking in response to technology-facilitated abuse. Journal of Interpersonal Violence, 38(11–12), 7063–7088. https://doi.org/10.1177/08862605221140042

21.

National Children’s Advocacy Center. (2021). Find A CAC – National children’s advocacy center. Find a CAC. Retrieved February 28, 2023, from https://www.nationalcac.org/find-a-cac/

22.

NCMEC. (2020). Be the solution: Helping victims of child sexual abuse material. https://go.missingkids.org/l/808593/2020-11-24/21bhf2

23.

Powell

Henry

(2018). Policing technology-facilitated sexual violence against adult victims: Police and service sector perspectives. Policing and Society, 28(3), 291–307. https://doi.org/10.1080/10439463.2016.1154964

24.

Prentice

D. A.

Carranza

(2002). What women and men should be, shouldn’t be, are allowed to be, and don’t have to be: The contents of prescriptive gender stereotypes. Psychology of Women Quarterly, 26(4), 269–281. https://doi.org/10.1111/1471-6402.t01-1-00066

25.

Rackley

McGlynn

Johnson

Henry

Gavey

Flynn

Powell

(2021). Seeking justice and redress for victim-survivors of image-based sexual abuse. Feminist Legal Studies, 29(3), 293–322. https://doi.org/10.1007/s10691-021-09460-8

26.

Ranapurwala

S. I.

Berg

M. T.

Casteel

(2016). Reporting crime victimizations to the police and the incidence of future victimizations: A longitudinal study. PLoS One, 11(7), e0160072. https://doi.org/10.1371/journal.pone.0160072

27.

Reyns

B. W.

Englebrecht

C. M.

(2010). The stalking victim’s decision to contact the police: A test of Gottfredson and Gottfredson’s theory of criminal justice decision making. Journal of Criminal Justice, 38(5), 998–1005. https://doi.org/10.1016/j.jcrimjus.2010.07.001

28.

Reyns

B. W.

Englebrecht

C. M.

(2014). Informal and formal help-seeking decisions of stalking victims in the United States. Criminal Justice and Behavior, 41(10), 1178–1194. https://doi.org/10.1177/0093854814541441

29.

Setter

Greene

Newman

Perry

(2021). Global threat assessment 2021 (pp. 1–100). WeProtect Global Alliance. https://www.weprotect.org/wp-content/uploads/Global-Threat-Assessment-2021.pdf

30.

The White House. (2022). Memorandum on establishment of the White House Task Force to address online harassment and abuse. Daily Compilation of Presidential Documents (pp. 1–4).

31.

Underwood

M. K.

Ehrenreich

S. E.

(2017). The power and the pain of adolescents’ digital communication: Cyber victimization and the perils of lurking. American Psychologist, 72, 144–158. https://doi.org/10.1037/a0040429

32.

Wemmers

J. A.

(2002). Restorative justice for victims of crime: A victim-oriented approach to restorative justice. International Review of Victimology, 9(1), 43–59.

33.

Wemmers

J. A.

(2013). Victims’ experiences in the criminal justice system and their recovery from crime. International Review of Victimology, 19(3), 221–233.

34.

Witwer

A. R.

Langton

Vermeer

M. J. D.

Banks

Woods

Jackson

B. A.

(2020). Countering technology-facilitated abuse: Criminal justice strategies for combating nonconsensual pornography, sextortion, doxing, and swatting. RAND Corporation. https://www.rand.org/pubs/research_reports/RRA108-3.html

35.

Wolak

Finkelhor

(2016). Sextortion: Findings from a survey of 1,631 victims. Crimes Against Children Research Center. https://calio.dspacedirect.org/handle/11212/3037

36.

Wolak

Finkelhor

Walsh

Treitman

(2018). Sextortion of minors: Characteristics and dynamics. Journal of Adolescent Health, 62(1), 72–79. https://doi.org/10.1016/j.jadohealth.2017.08.014