Potential Risks Posed by Online Bots' Fake Reviews on Social Media Websites

Example1:

Online bots are not physical entities but reside on multiple social media platforms. With the sophisticated software programmes and algorithms backbone, online bots can optimally simulate human-being interaction online, spread countless misinformation and fake reviews that could have remarkable impacts on the credibility and authenticity of social media content (Marx et al. 2020). According to the statistical estimation in 2017, Twitter has around 23 million bots (approximate 8.5% of its users) and Facebook has nearly 140 million bots (5.5% of accounts) (Ferrara et al. 2016)

Firstly, fake reviews propagated by online bots can significantly mislead social media users. It is highlighted that average online users tend to believe what they see and others share reviews without doubting when it comes to stacked-information (Ferrara et al. 2016). Personally, I might also be influenced by some unidentifiable fake reviews when I shop online like Amazon. Those deliberately-created fake information can directly have detrimental effects on consumer’s perception and subsequential behaviours. Once consumers detect fake reviews, businesses might face reputation collapse and the loss of consumer loyalty.

Secondly, as the bots are operated by software programmes and can do repetitive and automated tasks, fraudsters can commit advertising fraud being that generate tons of fake clicks and reviews (done by bots) to deceptively indicate their tremendous traffic and deliberately charge the advertisers and other cost companies for a higher advertising fee. Indeed, they pay for fake traffic and non-human shoppers that cannot bolster revenue for their businesses. Engaging in such activities might lead to severe legal exposure as the fraudsters would be sued for fraudulent behaviours (Walkowski & Pompon 2019).

Reference:

Ferrara,  E. , Varol, O., Davis, C., Menczer, F., & Flammini, A. 2016 “The Rise of Social Bots,” Communications of the ACM, vol. 59, no. 7, pp. 96–104.

Marx, J., Brünker, F., Mirbabaie, M. & Hochstrate, E. 2020, Conspiracy Machines -- The Role of Social Bots during the COVID-19 Infodemic, Cornell University Library, arXiv.org, Ithaca.

Walkowski, D. and Pompon, R., 2019. Good Bots, Bad Bots, and What You Can Do About Both. F5 Labs. <https://www.f5.com/labs/articles/threat-intelligence/good-bots--bad-bots--and-what-you-can-do-about-both>, Viewed 8 March 2021.

Example2:

A “social bot” is an automated program used to engage in social media (SM), designed to mimic human behaviour. Classified as ‘good’ or ‘bad’, social bots account for a sizable percent of all accounts on SM. Primarily used to boost popularity with ‘follows’ and ‘likes’, they artificially enhance the popularity of a person, with a growing black market for ‘follower’ bots to be bought and sold (Cloudfare).

Unlike chat bots, social bots are not required to know natural language, making them less resource intensive and widely deployed. Bright Local (2017) reports 84% of consumers trust online reviews. Social bots have become a powerful tool to gain from this trust, with a history of influencing major events and markets as seen in the 2016 election where over 400,000 social bots generated 20% of political discussions on SM (Heinzman 2019).

Bots have also been shown to manipulate stock markets by flooding SM with manufactured good or bad news about a corporation, influencing stock price directions (Heinzman 2019). In some cases, identifying a bot can be difficult. There is a growing epidemic of negative fake reviews, having devastating effects on businesses, causing follower-disconnect, fractured trust in the brand and potentially business failure. ‘Bad’ bots can cost a company, slowing site responses, adding costs with fraudulent advertising charges, and sabotage reputation through bad reviews (Walkowski 2019).

Fake reviews are a symptom of a larger problem, namely, businesses’ growing need to draw the attention of consumers in a crowded online environment. Pressure is intense and tempts the use of fraudulent ‘follower’ bots. Ultimately, review-based models of SM platforms need to be re-evaluated to protect companies and consumers.

REFERENCES USED

Heinzman, A. 2019, How fake reviews are manipulating you online, How-To Geek, online, viewed 6^th March 2021, <https://www.howtogeek.com/407521/how-fake-reviews-are-manipulating-you-online/>

Local Consumer Review Survey 2017, Bright Local, online, viewed 7^th March 2021, <https://www. brightlocal.com/research/local-consumer-review-survey-2017/>

Walkowski, D. 2019, Good Bots, Bad Bots, and What You Can Do About Both, F5 Labs, online, viewed 7^th March 2021, <https://www.f5.com/labs/articles/threat-intelligence/good-bots--bad-bots--and-what-you-can-do-about-both>

What is a Bot 2021, Cloudflare Inc., online, viewed 6^th March 2021, <https://www.cloudflare.com /learning/bots/what-is-a-bot/>