Digital Bystander effect/intervention

The Digital bystander effect, is a term that has gained alongside advancements in digital technology and the widespread adoption of online platforms, which extend from the bystander effect. The bystander effect, is a social psychological phenomenon defined by the individual reduction in willingness to offer helping behavior or intervene with a victim in the presence of other bystander situation. Digital bystanders or cyber-bystanders, refer to individuals who have the opportunity to intervene when they witness internet abuse or harmful communications and digital bystander intervention, including preventing or cease an online harmful content (Davidovic et al., 2023; Butler et al., 2022).

Previous psychology research has focused on “real-world” settings and highly studied in natural and laboratory contexts (Fischer et al., 2011). Raising recent psychology research has focused on re-examining the bystander effect within online environments and varied factors in online settings, such as perceived severity, visual anonymity, number of bystanders, diffusion of responsibility, and type of cyberbully that influence digital bystander effects and cyber-bystander's intention to intervene (Guazzini et al., 2019).

Variables affecting digital bystander effect/ intervention

Recognition and perceived severity and cyberbullying

Current studies on online settings has applied the Bystander Intervention Model (BIM), which outlines five step in bystanders decision-making. Recent studies suggest that individuals’ recognition of and perception of the severity of events such as cyberbullying are strongly correlated to cyber-bystanders' tendency to intervene (Dillon & Bushman, 2015; Koehler & Weber, 2018; Huang et al., 2023).

Dillon and Bushman (2015) investigated the relationship between cyber-bystanders’ intention to intervene in cyberbullying. The study recruited 241 university students to participate in an experiment involving an online chatroom. Participants observed a cyberbullying scenario where a chat monitor responded with increasingly aggressive comments such as, “Figure it out yourself,” when another participant asked for help.The experiment assessed whether participants noticed the cyberbullying and intervened in the situation. The findings suggested that participants who recognized the cyberbullying were significantly more likely to intervene. (ß = −1.26, t(219) = −5.6, p < .001)

Moreover, Huang et al. (2023) outline the importance of the severity of the cyberbullying incident in computer-mediated communication (CMC) and cyber-bystander intention to intervene. Data were collected from 88 college students were presented with a cyberbullying scenario through Weibo news reports and comments that is about the government’s efforts to regulate the ownership of pet dogs. Participants were presented with a cyberbullying scenario of the Weibo comment with agreement of the news and a low and high severity aggressive response of others. Perceived severity and intention to intervene data would be measured by a 5 item measure and 7 items adapted from Bastiaensens et al. (2014) with a 7-point and 6-point Likert scale accordingly. The findings suggest that a high perceived severity condition correlated to a higher intention to intervene for cyber-bystanders. (t (78) = 3.77, p < 0.001). Showing cyber-bystanders more probable to intervene in a high perceived severity cyberbullying condition.

Consistent results have been reported in Bastiaensens et al. (2014) and Patterson et al. (2016), studies demonstrated a significant effect of cyberbullying incident severity on cyber-bystanders' behavioral intentions. These findings reported the positive correlation between perceived severity and the tendency of behavioral intervention which higher intervention intentions when cyber-bystanders perceived the incident as severe. However, existing studies in the field of online contexts are limited in their exploration of diverse social networking platforms. The aforementioned research primarily focuses on online platforms such as Facebook, Weibo, and online chatrooms, which limit examining cyber-bystander intentions on other online platforms such as Instagram.

Number of bystander and Diffusion of responsibility

Consistent to "real-world" settings, anonymity and physical isolation in online settings can also contribute to the diffusion of responsibility of cyber-bystanders (You & Lee, 2019). Recent studies illustrate the relation between the number of bystander and the diffusion of responsibility. Siegal (1972) also outlined a negative correlation between the number of bystanders and the intervention received, suggesting the more crowded a "real-world "context, the less likely bystanders were to provide help.

Research conducted by Markey (2000) focuses on the number of bystanders and diffusion of responsibility. The study examined the relationship between the number of individuals present in a chat room and participants' response times when help was requested. The findings found a correlation between the number of bystanders in CMC contexts and the diffusion of responsibility, which led to delays in intervention and response. Markey's study involved 4,833 participants across 400 different online chat groups. Participants were presented with a stimulus question, which was repeated every minute until a response was received. The findings revealed a weak but a correlation between group size and response time (r(400) = 0.14, p < 0.01). The study's regression line analyses further reveal the negative correlation between group size and response time to help requests.

Moreover, Obermaier et al. (2016) also examine the number of bystanders and diffusion of responsibility in an online context. The study recruited 85 college participants and exposed them to two posts: the first post was a request for lecture notes by group member "Michi". And the second post highlighted a cyberbullying comment regarding the original questioner "Michi", such as “you are good for nothing and really have no business in university at all”. The study was based on the 5-point Likert scale to show the correlation of the number of bystanders (as indicated by "seen by 24" or "seen by 5025" notifications) and participants' feelings of responsibility and willingness to intervene in a cyberbullying setting. The findings suggest that the number of bystanders influence cyber-bystander's feelings of responsibility and willingness to intervene (β=−.07, p=.61) (β = −.11, p = .31) in a cyberbullying scenario, but not as a direct and significant correlation.

A consistent finding was also found in a study conducted by Blair et al. (2005), which indicated the correlation between the number of bystanders, diffusion of responsibility, and intervention rate. Study recruited 400 graduate-level participants, each of whom received an email searching for assistance with an online library task from an unknown university student with an indication that 0, 1, 14, or 49 others were also contacted. Participants were randomly assigned into the aforementioned 4 conditions and examined the correlation between the number of present cyber-bystanders and responses for help. The study utilized a four-level coding scheme to categorize participants' responses to online requests for assistance based on the degree of help provided. 4 levels presented as an ordinal ranking, reflecting levels of helpfulness and intervention provided by the participant. The data revealed a negative correlation between the presence of a larger number of cyber-bystanders and the response rate of email. Suggesting that virtual diffusion can, to some extent, contribute to the diffusion of responsibility and and the digital bystander effect. However, the findings also demonstrate that increasing the number of bystanders did not directly contribute to a rise in unresponsiveness for cyber-bystander intervention.

Type of cyberbully and intention to intervene

Another factor influencing cyber-bystanders' intention to intervene is the type of cyberbullying. While previous studies suggested that an increase in the number of bystanders would decrease intervention likelihood. Holfeld (2014) found inconsistent findings depending on the type of cyberbullying; the study revealed that public forms of cyberbullying, such as spreading harmful information on social network site (SNS) platforms like Facebook, tend to increase cyber-bystander's intention to intervene due to a large presence of bystanders. In contrast, private forms of cyberbullying, such as direct emails or online chats, would diminish the likelihood of intervention due to the significant reduction in the presence of cyber-bystanders.

Protester with mask to avoid showing their face.

Visual anonymity

Visual anonymity in an online context is another variable that can affect the helping behavior or intervention of a bystander. As defined by Sabina Misoch, visual anonymity refers to the source of information that cannot be detected physically and visual anonymity in an online context in which individuals are not able to visually identified from other users in a text-based online chat program (Misoch, 2015; Finn, 2016).

Research done by Brody and Vangelisti (2015) identified a positive correlation between visual anonymity and reduced bystander likelihood of intervention in online contexts. The study collected data from 265 undergraduate students, who were asked to recall an experience in which someone they knew was targeted (e.g., hurtful actions or messages) on Facebook. Visual anonymity was measured using a 7-point Likert scale, where participants rated their agreement with statements about whether they believed the victim was aware of their presence as a bystander. The findings revealed that visual anonymity was positively associated with bystander non-intervention behavior (β = 0.27). As suggested by another study conducted by Lapidot-Lefler and Barak (2012), visual anonymity reduces the likelihood for bystanders to provide intervention in online contexts such as Facebook and email. There is a correlation between visual anonymity and diffusion of responsibility , which could affect the bystander effect in an online environment, which is consistent with the previous Markey (2000) findings and could intensify cyberbullying-related behaviour, such as anti-normative behaviour (Macaulay et al., 2022).

Further, previous study conducted by Latané and Nida (1981) focusing on "real-world" settings also support the aforementioned idea of the influence of visual anonymity on cyber-bystander intervention and the bystander effect in online contexts. The study outlined a positive correlation between reduced diffusion of responsibility and situations when the victim could see the bystanders. Spears and Postmes (2015) examined the cognitive and strategic dimensions of the Social Identity model of Deindividuation Effects (SIDE) to analyze the impact of anonymity on bystander intervention. Research suggested that anonymity in CMC enhances the salience of the group identity, which encourages individuals to conform to perceived social group norms. Individuals would then be less likely to intervene when inaction is perceived as the group norm. On the strategic dimension, the study found that in contexts where individuals belong to a powerful out-group, anonymity reduces accountability for their presence. This finding is particularly relevant to cyberbullying and online settings, where anonymity diminishes the likelihood of bystanders taking action to intervene.

Direct intervention and indirect intervention

Cyberbullying behavior and online context predicted both bystander direct and indirect intervention (Ferreira et al., 2020). However, DiFranzo et al. (2018) mentioned that compared to 'real-world' settings, cyber-bystanders are more inclined toward indirect interventions due to the number of factors in online environments. Dillon and Bushman (2015) define indirect intervention as including providing resources that ultimately resolve problems or help the victims such as reporting a harmful post in the SNS. In addition, Dillon and Bushman (2015) and Shultz et al. (2014) indicated cyber-bystanders would utilize more indirect intervention in online settings and cyberbullying contexts compared to bystanders in "real-world' settings. This was proven by another research study conducted by Obermaier (2024), which found a congruent finding that compares to "real-world" bystanders direct intervention, such as counter-speech. Cyber-bystander intention to provide indirect intervention, such as reporting against online contexts such as bullying, aggressive or hate speech.

Conclusion

To conclude, this article covered the gap from the traditional bystander effect Wikipedia article. This article illustrates the digital bystander effect and highlights how the online environment influences cyber-bystanders willingness to intervene, especially in harmful online incidents such as cyberbullying. Key variables impacting cyber-bystander's intervention include perceived severity, visual anonymity, diffusion of responsibility, the number of bystanders, and the type of cyberbullying. Numerous studies suggest that the more the number of cyber-bystander present, the less likely the intention to intervene. Additionally, some studies suggest the public and private forms of cyberbullying also interfere cyber-bystanders intervention with factor such as visibility, number of cyber-bystanders present, and social accountability play a vital role. Moreover, visual anonymity also significantly diminishes a cyber-bystander's accountability and willingness to intervene.

Likewise, cyber-bystanders tend to favor indirect intervention strategies, such as reporting harmful content compared to traditional bystander. Current research is limited on examining a few SNS online platforms such as Facebook, Weibo, and email. This contributed to research gaps in understanding cyber-bystanders’ intervention behaviors across contemporary and popular online platforms like Instagram.

References

Bastiaensens, S., Vandebosch, H., Poels, K., Van Cleemput, K., DeSmet, A., & De Bourdeaudhuij, I. (2014). Cyberbullying on social network sites. An experimental study into bystanders’ behavioural intentions to help the victim or reinforce the bully. Computers in Human Behavior, 31, 259–271. https://doi.org/10.1016/j.chb.2013.10.036

Blair, C. A., Foster Thompson, L., & Wuensch, K. L. (2005). Electronic Helping Behavior: The Virtual Presence of Others Makes a Difference. Basic and Applied Social Psychology, 27(2), 171–178. https://doi.org/10.1207/s15324834basp2702_8

Brody, N., & Vangelisti, A. L. (2016). Bystander Intervention in Cyberbullying. Communication Monographs, 83(1), 94–119. https://doi.org/10.1080/03637751.2015.1044256

Butler, L. C., Graham, A., Fisher, B. S., Henson, B., & Reyns, B. W. (2022). Examining the Effect of Perceived Responsibility on Online Bystander Intervention, Target Hardening, and Inaction. Journal of Interpersonal Violence, 37(21–22), NP20847–NP20872. https://doi.org/10.1177/08862605211055088

Davidovic, A., Talbot, C., Hamilton-Giachritsis, C., Joinson, A., & Pearce, K. (2023). To intervene or not to intervene: young adults’ views on when and how to intervene in online harassment. Journal of Computer-Mediated Communication, 28(5). https://doi.org/10.1093/jcmc/zmad027

DiFranzo, D., Taylor, S. H., Kazerooni, F., Wherry, O. D., & Bazarova, N. N. (2018). Upstanding by Design: Bystander Intervention in Cyberbullying. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–12. https://doi.org/10.1145/3173574.3173785

Dillon, K. P., & Bushman, B. J. (2015). Unresponsive or un-noticed?: Cyberbystander intervention in an experimental cyberbullying context. Computers in Human Behavior, 45, 144–150. https://doi.org/10.1016/j.chb.2014.12.009

Ferreira, P. C., Simão, A. M. V., Paiva, A., & Ferreira, A. (2020). Responsive bystander behaviour in cyberbullying: a path through self-efficacy. Behaviour & Information Technology, 39(5), 511–524. https://doi.org/10.1080/0144929X.2019.1602671

Finn, E. M. (2016). Negatively Disinhibited Online Communication: The Role of Visual Anonymity and Public Self-Awareness [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461142960

Fischer, P., Krueger, J. I., Greitemeyer, T., Vogrincic, C., Kastenmuller, A., Frey, D., Heene, M., Wicher, M., & Kainbacher, M. (2011). The Bystander-Effect: A Meta-Analytic Review on Bystander Intervention in Dangerous and Non-Dangerous Emergencies. Psychological Bulletin, 137(4), 517–537. https://doi.org/10.1037/a0023304

Guazzini, A., Imbimbo, E., Stefanelli, F., Bravi, G., Pacini, G., El Yacoubi, S., Bagnoli, F., El Yacoubi, S., Pacini, G., & Bagnoli, F. (2019). The Online Bystander Effect: Evidence from a Study on Synchronous Facebook Communications. In Internet Science (Vol. 11938, pp. 153–167). Springer International Publishing AG. https://doi.org/10.1007/978-3-030-34770-3_12

Holfeld, B. (2014). Perceptions and attributions of bystanders to cyber bullying. Computers in Human Behavior, 38, 1–7. https://doi.org/10.1016/j.chb.2014.05.012

Huang, L., Li, W., Xu, Z., Sun, H., Ai, D., Hu, Y., Wang, S., Li, Y., & Zhou, Y. (2023). The Severity of Cyberbullying Affects Bystander Intervention Among College Students: The Roles of Feelings of Responsibility and Empathy. Psychology Research and Behavior Management, 16, 893–903. https://doi.org/10.2147/PRBM.S397770

Koehler, C., & Weber, M. (2018). Do I really need to help?!” Perceived severity of cyberbullying, victim blaming, and bystanders’ willingness to help the victim. Cyberpsychology, 12(4). https://doi.org/10.5817/CP2018-4-4

Lapidot-Lefler, N., & Barak, A. (2012). Effects of anonymity, invisibility, and lack of eye-contact on toxic online disinhibition. Computers in Human Behavior, 28(2), 434–443. https://doi.org/10.1016/j.chb.2011.10.014

Latané, B., & Nida, S. (1981). Ten years of research on group size and helping. Psychological Bulletin, 89(2), 308–324. https://doi.org/10.1037/0033-2909.89.2.308'

Macaulay, P. J. R., Betts, L. R., Stiller, J., & Kellezi, B. (2022). Bystander responses to cyberbullying: The role of perceived severity, publicity, anonymity, type of cyberbullying, and victim response. Computers in Human Behavior, 131, 107238-. https://doi.org/10.1016/j.chb.2022.107238

Markey, P. M. (2000). Bystander intervention in computer-mediated communication. Computers in Human Behavior, 16(2), 183–188. https://doi.org/10.1016/S0747-5632(99)00056-4

Misoch, S. (2015). Stranger on the internet: Online self-disclosure and the role of visual anonymity. Computers in Human Behavior, 48, 535–541. https://doi.org/10.1016/j.chb.2015.02.027

Nixon C. L. (2014). Current perspectives: the impact of cyberbullying on adolescent health. Adolescent health, medicine and therapeutics, 5, 143–158. https://doi.org/10.2147/AHMT.S36456

Obermaier, M. (2024). Youth on standby? Explaining adolescent and young adult bystanders’ intervention against online hate speech. New Media & Society, 26(8), 4785–4807. https://doi.org/10.1177/14614448221125417

Obermaier, M., Fawzi, N., & Koch, T. (2016). Bystanding or standing by? How the number of bystanders affects the intention to intervene in cyberbullying. New Media & Society, 18(8), 1491–1507. https://doi.org/10.1177/1461444814563519

Patterson, L. J., Allan, A., & Cross, D. (2017). Adolescent Bystander Behavior in the School and Online Environments and the Implications for Interventions Targeting Cyberbullying. Journal of School Violence, 16(4), 361–375. https://doi.org/10.1080/15388220.2016.1143835

Peebles E. (2014). Cyberbullying: Hiding behind the screen. Paediatrics & child health, 19(10), 527–528. https://doi.org/10.1093/pch/19.10.527

Shultz, E., Heilman, R., & Hart, K. J. (2014). Cyber-bullying: An exploration of bystander behavior and motivation. Cyberpsychology, 8(4). https://doi.org/10.5817/CP2014-4-3

Siegal, H. A. (1972). The Unresponsive Bystander: Why Doesn’t He Help? [Review of The Unresponsive Bystander: Why Doesn’t He Help?]. Contemporary Sociology, 1(3), 226–227. American Sociological Association. https://doi.org/10.2307/2063973

Spears, R., Postmes, T., & Sundar, S. S. (2015). Group Identity, Social Influence, and Collective Action Online. In The Handbook of the Psychology of Communication Technology (pp. 23–46). John Wiley & Sons, Ltd. https://doi.org/10.1002/9781118426456.ch2

Yao, M. Z., & Ling, R. (2020). “What Is Computer-Mediated Communication?”—An Introduction to the Special Issue. Journal of Computer-Mediated Communication, 25(1), 4–8. https://doi.org/10.1093/jcmc/zmz027

You, L., & Lee, Y.-H. (2019). The bystander effect in cyberbullying on social network sites: Anonymity, group size, and intervention intentions. Telematics and Informatics, 45, 101284-. https://doi.org/10.1016/j.tele.2019.101284

Yu, B. (2011). Computer-Mediated Communication Systems. TripleC, 9(2), 531–534. https://doi.org/10.31269/vol9iss2pp531-534

User:Ong Chi Chin/sandbox