Social bot

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A social bot, also described as a social AI or social algorithm, is a software agent that communicates autonomously on social media. The messages (e.g. tweets) it distributes can be simple and operate in groups and various configurations with partial human control (hybrid) via algorithm. Social bots can also use artificial intelligence and machine learning to express messages in more natural human dialogue.

Contents

Uses

Some of the uses for social bots are:

Another example is that the bots can be used for algorithmic curation, algorithmic radicalization, and/or influence-for-hire, a term that refers to the selling of an account on social media platforms.

History

Bots have coexisted with computer technology since its creation. Social bots have therefore risen in popularity simultaneously with the rise of social media. Social bots, besides being able to (re-)produce or reuse messages autonomously, also share many traits with spambots concerning their tendency to infiltrate large user groups. [3] Artificial Social Networking Intelligence (ASNI) refers to the application of artificial intelligence within social networking services and social media platforms. ASNI is expected to evolve rapidly.

Twitterbots are already well-known examples, but corresponding autonomous agents on Facebook and elsewhere have also been observed. Using social bots is against the terms of service of many platforms, such as Twitter and Instagram, although it is allowed to some degree by others, such as Reddit and Discord. Even for social media platforms that restrict social bots, a certain degree of automation is intended by making social media APIs available. Social media platforms have also developed their own automated tools to filter out messages that come from bots, although they cannot detect all bot messages. [4]

Twitter bots posting similar messages during the 2016 United States elections Twitter bots 2016-11-13.png
Twitter bots posting similar messages during the 2016 United States elections

Due to the difficulty of recognizing social bots and separating them from "eligible" automation via social media APIs, it is unclear how legal regulation can be enforced. Social bots are expected to play a role in the future shaping of public opinion by autonomously acting as incessant influencers. Some social bots have manipulated public opinions (especially in a political sense), stock market manipulation, advertisements, and the malicious extortion of spear-phishing attempts. [5]

Detection

The first generation of bots could sometimes be distinguished from real users by their often superhuman capacities to post messages. Later developments have succeeded in imprinting more "human" activity and behavioral patterns in the agent. With enough bots, it might be even possible to achieve artificial social proof. To unambiguously detect social bots as what they are, a variety of criteria [6] must be applied together using pattern detection techniques, some of which are: [7]

Social bots are always becoming increasingly difficult to detect and understand. The bots' human-like behavior, ever-changing behavior of the bots, and the sheer volume of bots covering every platform may have been a factor in the challenges of removing them. [11] Social media sites, like Twitter, are among the most affected, with CNBC reporting up to 48 million of the 319 million users (roughly 15%) were bots in 2017. [12]

Botometer [13] (formerly BotOrNot) is a public Web service that checks the activity of a Twitter account and gives it a score based on how likely the account is to be a bot. The system leverages over a thousand features. [14] [15] An active method for detecting early spam bots was to set up honeypot accounts that post nonsensical content, which may get reposted (retweeted) by the bots. [16] However, bots evolve quickly, and detection methods have to be updated constantly, because otherwise they may get useless after a few years. [17] One method is the use of Benford's Law for predicting the frequency distribution of significant leading digits to detect malicious bots online. This study was first introduced at the University of Pretoria in 2020. [18] Another method is artificial-intelligence-driven detection. Some of the sub-categories of this type of detection would be active learning loop flow, feature engineering, unsupervised learning, supervised learning, and correlation discovery. [11]

Some operations of bots work together in a synchronized way. For example, ISIS used Twitter to amplify its Islamic content by numerous orchestrated accounts which further pushed an item to the Hot List news, [19] thus further amplifying the selected news to a larger audience. [20] This mode of synchronized bots accounts can be used as a tool of propaganda as well as stock markets manipulations. [21]

Platforms

Instagram

Instagram reached a billion active monthly users in June 2018, [22] but of those 1 billion active users, it was estimated that up to 10% were being run by automated social bots. While malicious propaganda posting bots are still popular, many individual users use engagement bots to propel themselves to a false virality, making them seem more popular on the app. These engagement bots can like, watch, follow, and comment on the users' posts. [23]

Around the same time, the platform achieved the 1 billion monthly user plateau. Facebook (Instagram and WhatsApp's parent company) planned to hire 10,000 to provide additional security to their platforms; this would include combatting the rising number of bots and malicious posts on the platforms. [24] Due to increased security on the platform and the detection methods used by Instagram, some botting companies are reporting issues with their services because Instagram imposes interaction limit thresholds based on past and current app usage, and many payment and email platforms deny the companies access to their services, preventing potential clients from being able to purchase them. [25]

Twitter

Twitter's bot problem is caused by the ease of creating and maintaining them. The ease of creating the account as and the many APIs that allow for complete automation of the accounts are leading to excessive amounts of organizations and individuals using these tools to push their own needs. [12] [26] CNBC claimed that about 15% of the 319 million Twitter users in 2017 were bots; the exact number is 48 million. [12] As of July 7, 2022, Twitter is claiming that they remove 1 million spam bots from their platform every day. [27]

Some bots are used to automate scheduled tweets, download videos, set reminders and send warnings of natural disasters. [28] Those are examples of bot accounts, but Twitter's API allows for real accounts (individuals or organizations) to use certain levels of bot automation on their accounts and even encourages the use of them to improve user experiences and interactions. [29]

See also

Related Research Articles

Malware is any software intentionally designed to cause disruption to a computer, server, client, or computer network, leak private information, gain unauthorized access to information or systems, deprive access to information, or which unknowingly interferes with the user's computer security and privacy. Researchers tend to classify malware into one or more sub-types.

<span class="mw-page-title-main">Spamming</span> Unsolicited electronic messages, especially advertisements

Spamming is the use of messaging systems to send multiple unsolicited messages (spam) to large numbers of recipients for the purpose of commercial advertising, non-commercial proselytizing, or any prohibited purpose, or simply repeatedly sending the same message to the same user. While the most widely recognized form of spam is email spam, the term is applied to similar abuses in other media: instant messaging spam, Usenet newsgroup spam, Web search engine spam, spam in blogs, wiki spam, online classified ads spam, mobile phone messaging spam, Internet forum spam, junk fax transmissions, social spam, spam mobile apps, television advertising and file sharing spam. It is named after Spam, a luncheon meat, by way of a Monty Python sketch about a restaurant that has Spam in almost every dish in which Vikings annoyingly sing "Spam" repeatedly.

Astroturfing is the deceptive practice of hiding the sponsors of an orchestrated message or organization to make it appear as though it originates from, and is supported by, unsolicited grassroots participants. It is a practice intended to give the statements or organizations credibility by withholding information about the source's financial backers.

Messaging spam, sometimes called SPIM, is a type of spam targeting users of instant messaging (IM) services, SMS, or private messages within websites.

<span class="mw-page-title-main">Botnet</span> Collection of compromised internet-connected devices controlled by a third party

A botnet is a group of Internet-connected devices, each of which runs one or more bots. Botnets can be used to perform distributed denial-of-service (DDoS) attacks, steal data, send spam, and allow the attacker to access the device and its connection. The owner can control the botnet using command and control (C&C) software. The word "botnet" is a portmanteau of the words "robot" and "network". The term is usually used with a negative or malicious connotation.

An Internet bot, web robot, robot or simply bot, is a software application that runs automated tasks (scripts) on the Internet, usually with the intent to imitate human activity, such as messaging, on a large scale. An Internet bot plays the client role in a client–server model whereas the server role is usually played by web servers. Internet bots are able to perform simple and repetitive tasks much faster than a person could ever do. The most extensive use of bots is for web crawling, in which an automated script fetches, analyzes and files information from web servers. More than half of all web traffic is generated by bots.

<span class="mw-page-title-main">Misinformation</span> Incorrect or misleading information

Misinformation is incorrect or misleading information. Misinformation can exist without specific malicious intent; disinformation is distinct in that it is deliberately deceptive and propagated. Misinformation can include inaccurate, incomplete, misleading, or false information as well as selective or half-truths. In January 2024, the World Economic Forum identified misinformation and disinformation, propagated by both internal and external interests, to "widen societal and political divides" as the most severe global risks within the next two years.

On Internet usage, an email bomb is a form of net abuse that sends large volumes of email to an address to overflow the mailbox, overwhelm the server where the email address is hosted in a denial-of-service attack or as a smoke screen to distract the attention from important email messages indicating a security breach.

<span class="mw-page-title-main">Social media</span> Virtual online communities

Social media are interactive technologies that facilitate the creation, sharing and aggregation of content amongst virtual communities and networks. Common features include:

Social media in the fashion industry refers to the use of social media platforms by fashion designers and users to promote and participate in trends. Over the past several decades, the development of social media has increased along with its usage by consumers. The COVID-19 pandemic was a sharp turn of reliance on the virtual sphere for the industry and consumers alike. Social media has created new channels of advertising for fashion houses to reach their target markets. Since its surge in 2009, luxury fashion brands have used social media to build interactions between the brand and its customers to increase awareness and engagement. The emergence of influencers on social media has created a new way of advertising and maintaining customer relationships in the fashion industry. Numerous social media platforms are used to promote fashion trends, with Instagram and TikTok being the most popular among Generation Y and Z. The overall impact of social media in the fashion industry included the creation of online communities, direct communication between industry leaders and consumers, and criticized ideals that are promoted by the industry through social media.

Shadow banning, also called stealth banning, hellbanning, ghost banning, and comment ghosting, is the practice of blocking or partially blocking a user or the user's content from some areas of an online community in such a way that the ban is not readily apparent to the user, regardless of whether the action is taken by an individual or an algorithm. For example, shadow-banned comments posted to a blog or media website would be visible to the sender, but not to other users accessing the site.

Social spam is unwanted spam content appearing on social networking services, social bookmarking sites, and any website with user-generated content. It can be manifested in many ways, including bulk messages, profanity, insults, hate speech, malicious links, fraudulent reviews, fake friends, and personally identifiable information.

The term twitter bomb or tweet bomb refers to posting numerous Tweets with the same hashtags and other similar content, including @messages, from multiple accounts, with the goal of advertising a certain meme, usually by filling people's Tweet feeds with the same message, and making it a "trending topic" on Twitter. This may be done by individual users, fake accounts, or both.

A Twitter bot is a type of software bot that controls a Twitter account via the Twitter API. The social bot software may autonomously perform actions such as tweeting, retweeting, liking, following, unfollowing, or direct messaging other accounts. The automation of Twitter accounts is governed by a set of automation rules that outline proper and improper uses of automation. Proper usage includes broadcasting helpful information, automatically generating interesting or creative content, and automatically replying to users via direct message. Improper usage includes circumventing API rate limits, violating user privacy, spamming, and sockpuppeting. Twitter bots may be part of a larger botnet. They can be used to influence elections and in misinformation campaigns.

<span class="mw-page-title-main">Filippo Menczer</span> American and Italian computer scientist

Filippo Menczer is an American and Italian academic. He is a University Distinguished Professor and the Luddy Professor of Informatics and Computer Science at the Luddy School of Informatics, Computing, and Engineering, Indiana University. Menczer is the Director of the Observatory on Social Media, a research center where data scientists and journalists study the role of media and technology in society and build tools to analyze and counter disinformation and manipulation on social media. Menczer holds courtesy appointments in Cognitive Science and Physics, is a founding member and advisory council member of the IU Network Science Institute, a former director the Center for Complex Networks and Systems Research, a senior research fellow of the Kinsey Institute, a fellow of the Center for Computer-Mediated Communication, and a former fellow of the Institute for Scientific Interchange in Turin, Italy. In 2020 he was named a Fellow of the ACM.

Ghost followers, also referred to as ghosts and ghost accounts or lurkers, are users on social media platforms who remain inactive or do not engage in activity. They register on platforms such as Twitter and Instagram. These users follow active members, but do not partake in liking, commenting, messaging, and posting. These accounts may be created by people or by social bots.

Social media mining is the process of obtaining data from user-generated content on social media in order to extract actionable patterns, form conclusions about users, and act upon the information. Mining supports targeting advertising to users or academic research. The term is an analogy to the process of mining for minerals. Mining companies sift through raw ore to find the valuable minerals; likewise, social media mining sifts through social media data in order to discern patterns and trends about matters such as social media usage, online behaviour, content sharing, connections between individuals, buying behaviour. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as such organizations can use the analyses for tasks such as design strategies, introduce programs, products, processes or services.

Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems in industrial applications.

Location inference is the method of identifying the location profiles of users on social media platforms such as Twitter and Facebook from their message content, friends' network and social interaction even when they did not explicitly disclose such on their account profiles or geotag their messages.

Emilio Ferrara is an Italian-American computer scientist, researcher, and professor in the field of data science and social networks. As of 2022, he serves as a Full Professor at the University of Southern California (USC), in the Viterbi School of Engineering and USC Annenberg School for Communication, where he conducts research on computational social science, network science, and machine learning. Ferrara is known for his work in the detection of social bots and the analysis of misinformation on social media platforms.

References

  1. ↑ "The influence of social bots". www.akademische-gesellschaft.com. Retrieved March 1, 2022.
  2. ↑ Frederick, Kara (2019). "The New War of Ideas: Counterterrorism Lessons for the Digital Disinformation Fight". Center for a New American Security.{{cite journal}}: Cite journal requires |journal= (help)
  3. ↑ Ferrara, Emilio; Varol, Onur; Davis, Clayton; Menczer, Filippo; Flammini, Alessandro (June 24, 2016). "The rise of social bots". Communications of the ACM. 59 (7): 96–104. arXiv: 1407.5225 . doi:10.1145/2818717. ISSN   0001-0782. S2CID   1914124.
  4. ↑ Efthimion, Phillip; Payne, Scott; Proferes, Nicholas (July 20, 2018). "Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots". SMU Data Science Review. 1 (2).
  5. ↑ Gorwa, Robert; Guilbeault, Douglas (June 2020). "Unpacking the Social Media Bot: A Typology to Guide Research and Policy". Policy & Internet. 12 (2): 225–248. arXiv: 1801.06863 . doi:10.1002/poi3.184. ISSN   1944-2866. S2CID   51877148.
  6. ↑ Dewangan, Madhuri; Rishabh Kaushal (2016). "SocialBot: Behavioral Analysis and Detection". International Symposium on Security in Computing and Communication. doi:10.1007/978-981-10-2738-3_39.
  7. ↑ Ferrara, Emilio; Varol, Onur; Davis, Clayton; Menczer, Filippo; Flammini, Alessandro (2016). "The Rise of Social Bots". Communications of the ACM. 59 (7): 96–104. arXiv: 1407.5225 . doi:10.1145/2818717. S2CID   1914124.
  8. ↑ Mazza, Michele; Stefano Cresci; Marco Avvenuti; Walter Quattrociocchi; Maurizio Tesconi (2019). "RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter". In Proceedings of the 10th ACM Conference on Web Science (WebSci '19). arXiv: 1902.04506 . doi:10.1145/3292522.3326015.
  9. ↑ "How to Find and Remove Fake Followers from Twitter and Instagram : Social Media Examiner".
  10. ↑ Weishampel, Anthony; Staicu, Ana-Maria; Rand, William (March 1, 2023). "Classification of social media users with generalized functional data analysis". Computational Statistics & Data Analysis. 179: 107647. doi: 10.1016/j.csda.2022.107647 . ISSN   0167-9473. S2CID   253359560.
  11. 1 2 Zago, Mattia; Nespoli, Pantaleone; Papamartzivanos, Dimitrios; Perez, Manuel Gil; Marmol, Felix Gomez; Kambourakis, Georgios; Perez, Gregorio Martinez (August 2019). "Screening Out Social Bots Interference: Are There Any Silver Bullets?". IEEE Communications Magazine. 57 (8): 98–104. doi:10.1109/MCOM.2019.1800520. ISSN   1558-1896. S2CID   201623201.
  12. 1 2 3 Newberg, Michael (March 10, 2017). "As many as 48 million Twitter accounts aren't people, says study". CNBC. Retrieved November 22, 2022.
  13. ↑ "Botometer".
  14. ↑ Davis, Clayton A.; Onur Varol; Emilio Ferrara; Alessandro Flammini; Filippo Menczer (2016). "BotOrNot: A System to Evaluate Social Bots". Proc. WWW Developers Day Workshop. arXiv: 1602.00975 . doi:10.1145/2872518.2889302.
  15. ↑ Varol, Onur; Emilio Ferrara; Clayton A. Davis; Filippo Menczer; Alessandro Flammini (2017). "Online Human-Bot Interactions: Detection, Estimation, and Characterization". Proc. International AAAI Conf. on Web and Social Media (ICWSM).
  16. ↑ "How to Spot a Social Bot on Twitter". technologyreview.com. July 28, 2014. Social bots are sending a significant amount of information through the Twittersphere. Now there's a tool to help identify them
  17. ↑ Grimme, Christian; Preuss, Mike; Adam, Lena; Trautmann, Heike (2017). "Social Bots: Human-Like by Means of Human Control?". Big Data. 5 (4): 279–293. arXiv: 1706.07624 . doi:10.1089/big.2017.0044. PMID   29235915. S2CID   10464463.
  18. ↑ Mbona, Innocent; Eloff, Jan H. P. (January 1, 2022). "Feature selection using Benford's law to support detection of malicious social media bots". Information Sciences. 582: 369–381. doi:10.1016/j.ins.2021.09.038. hdl: 2263/82899 . ISSN   0020-0255. S2CID   240508186.
  19. ↑ Giummole, Federica; Orlando, Salvatore; Tolomei, Gabriele (2013). "Trending Topics on Twitter Improve the Prediction of Google Hot Queries". 2013 International Conference on Social Computing. IEEE. pp. 39–44. doi:10.1109/socialcom.2013.12. ISBN   978-0-7695-5137-1. S2CID   15657978.
  20. ↑ Badawy, Adam; Ferrara, Emilio (April 3, 2018). "The rise of Jihadist propaganda on social networks". Journal of Computational Social Science. 1 (2): 453–470. arXiv: 1702.02263 . doi:10.1007/s42001-018-0015-z. ISSN   2432-2717. S2CID   13122114.
  21. ↑ Sela, Alon; Milo, Orit; Kagan, Eugene; Ben-Gal, Irad (November 15, 2019). "Improving information spread by spreading groups". Online Information Review. 44 (1): 24–42. doi:10.1108/oir-08-2018-0245. ISSN   1468-4527. S2CID   211051143.
  22. ↑ Constine, Josh (June 20, 2018). "Instagram hits 1 billion monthly users, up from 800M in September". TechCrunch. Retrieved November 24, 2022.
  23. ↑ "Instagram Promotion Service (Real Marketing) – UseViral". August 15, 2021. Retrieved November 24, 2022.
  24. ↑ "Instagram's Growing Bot Problem". The Information. July 18, 2018. Retrieved November 24, 2022.
  25. ↑ Morales, Eduardo (March 8, 2022). "Instagram Bots in 2021 — Everything You Need To Know". Medium. Retrieved November 24, 2022.
  26. ↑ Gilani, Zafar; Farahbakhsh, Reza; Crowcroft, Jon (April 3, 2017). "Do Bots impact Twitter activity?". Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion. Republic and Canton of Geneva, CHE: International World Wide Web Conferences Steering Committee. pp. 781–782. doi:10.1145/3041021.3054255. ISBN   978-1-4503-4914-7. S2CID   33003478.
  27. ↑ Dang, Sheila; Paul, Katie (July 7, 2022). "Twitter says it removes over 1 million spam accounts each day". Reuters. Retrieved November 23, 2022.
  28. ↑ Azhar, Huzaifa (December 10, 2021). "10 Best Twitter Bots You Should Follow in 2022 - TechPP". techpp.com. Retrieved November 24, 2022.
  29. ↑ "Twitter's automation development rules | Twitter Help". help.twitter.com. Retrieved November 24, 2022.
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