Social bot

Last updated

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.[ citation needed ]

Contents

Uses

Lutz Finger identifies five immediate uses for social bots: [2] [ clarification needed ]

Some of another examples, such as:

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. [4]

Artificial Social Networking Intelligence (ASNI) refers to the application of artificial intelligence within social networking services and social media platforms. It encompasses various technologies and techniques used to automate, personalize, enhance, improve, and synchronize user's interactions and experiences within social networks. ASNI is expected to evolve rapidly, influencing how we interact online and shaping their digital experiences. Transparency, ethical considerations, media influence bias, and user control over data will be crucial to ensure responsible development and positive impact.

Twitterbots are already well-known examples, but corresponding autonomous agents on Facebook and elsewhere have also been observed. Nowadays, social bots are equipped with or can generate convincing internet personas that are well capable of influencing real people.

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 of course 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 are not advanced enough to detect all bot messages. [5]

The topic of legal regulation of social bots is becoming more urgent to policy makers in many countries, however, due to the difficulty of recognizing social bots and separating them from "eligible" automation via social media APIs, it is currently unclear how that can be done and also if it can be enforced. In any case, social bots are expected to play a role in the future shaping of public opinion by autonomously acting as incessant and never-tiring influencer. Leading up to the present day, the impact of social bots has grown so much that they are now affecting society through social media, by manipulating public opinions (especially in a political sense, which is considered a sub-category of social bots called political bots), stock market manipulation, concealed advertisements, and malicious extortion of spear-phishing attempts which are why there has been an emergence of urgency to create more research, policies, and detection of bots on the many platforms that they affect. [6]

Detection

The first generation of bots could sometimes be distinguished from real users by their often superhuman capacities to post messages around the clock (and at massive rates). 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 [7] must be applied together using pattern detection techniques, some of which are: [8]

Social bots are always becoming increasingly difficult to detect and understand, some of the greatest challenges for the detection of bots include: social big data, modern social bots datasets, detect the bots' human-like behavior in the wild, ever-changing behavior of the bots, lack of appropriate visualization tools and the sheer volume of bots covering every platform. [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 that worked well in detecting early spam bots was to set up honeypot accounts where obvious nonsensical content was posted and then dumbly reposted (retweeted) by bots. [16] However, recent studies [17] show that bots evolve quickly and detection methods have to be updated constantly, because otherwise they may get useless after a few years.

One method still in development, but showing promise 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 and had successful trials in the field. [18]

Another method that has also proven to be quite successful in research and in the field is artificial-intelligence-driven detection which simply put, evens the playing field when putting artificial intelligence against itself. Some of the most popular sub-categories of this type of detection would be active learning loop flow, feature engineering, unsupervised learning and outliers identification, supervised learning, correlation discovery, and system adaptability. [12]

An important mode of operation of bots is by working 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 is an efficient method to further spread a desired news and is also used as a modern tool of propaganda as well as stock markets manipulations. [21]

Research and development to detect malicious bots continue to be an important topic throughout the tech world. Social media sites like Twitter, which are among the most affected with CNBC reporting up to 48 million of the 319 million users (roughly 15%) were bots in 2017, continue to fight against the spread of misinformation, scams and other harmful activities on their platforms. [22]

Platforms

Instagram

Instagram reached a billion active monthly users in June 2018, [23] but of those 1 billion active users it was estimated that up to 10% were being run by automated social bots. Instagram's unique platform for sharing pictures and videos makes it one of the biggest targets for malicious social bot attacks, especially porn bot accounts, [24] because imagery resonates with the platform's users more than simple words on platforms like Twitter. [25] 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 do everything from like, watch, follow, and comment on the users' posts. [26] Around the same time that 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. [25] Due to increased security on the platform and enhanced detecting methods 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. [27]

Twitter

Twitter's bot problem is being caused by the ease of use in creating and maintaining them. To create an account you must have a phone number, email address, and CAPTCHA recognition. 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. [22] [28] CNBC claiming that about 15% of the 319 million Twitter users in 2017 were bots, the exact number is 48 million. [22] As of July 7, 2022, Twitter is claiming that they remove 1 million spam bots on their platform each and every day. [29] Twitter bots are not all malicious, some bots are used to automate scheduled tweets, download videos, set reminders and even send warnings of natural disasters. [30] 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. [31]

See also

Related Research Articles

Astroturfing is the practice of hiding the sponsors of a message or organization to make it appear as though it originates from, and is supported by, grassroots participants. It is a practice intended to give the statements or organizations credibility by withholding information about the source's financial backers. The term astroturfing is derived from AstroTurf, a brand of synthetic carpeting designed to resemble natural grass, as a play on the word "grassroots". The implication behind the use of the term is that instead of a "true" or "natural" grassroots effort behind the activity in question, there is a "fake" or "artificial" appearance of support.

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

CRM114 is a program based upon a statistical approach for classifying data, and especially used for filtering email spam.

<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.

VoIP spam or SPIT is unsolicited, automatically dialed telephone calls, typically using voice over Internet Protocol (VoIP) technology.

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.

Misinformation is incorrect or misleading information. It differs from disinformation, which is deliberately deceptive and propagated information. Early definitions of misinformation focused on statements that were patently false, incorrect, or not factual. Therefore, a narrow definition of misinformation refers to the information's quality, whether inaccurate, incomplete, or false. However, recent studies define misinformation per deception rather than informational accuracy because misinformation can include falsehoods, selective truths, and half-truths.

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, ideas, interests, and other forms of expression through virtual communities and networks. Social media refers to new forms of media that involve interactive participation. While challenges to the definition of social media arise due to the variety of stand-alone and built-in social media services currently available, there are some common features:

  1. Social media are interactive Web 2.0 Internet-based applications.
  2. User-generated content—such as text posts or comments, digital photos or videos, and data generated through all online interactions—is the lifeblood of social media.
  3. Users create service-specific profiles for the website or app that are designed and maintained by the social media organization.
  4. Social media helps the development of online social networks by connecting a user's profile with those of other individuals or groups.

Social network advertising, also known as "social media targeting," is a group of terms used to describe forms of online advertising and digital marketing focusing on social networking services. One of the significant benefits of this type of advertising is that advertisers can take advantage of the users' demographic information, psychographics and other data points to target their ads appropriately.

<span class="mw-page-title-main">Social media marketing</span> Promotion of products or services on social media

Social media marketing is the use of social media platforms and websites to promote a product or service. Although the terms e-marketing and digital marketing are still dominant in academia, social media marketing is becoming more popular for both practitioners and researchers. Most social media platforms have built-in data analytics tools, enabling companies to track the progress, success, and engagement of social media marketing campaigns. Companies address a range of stakeholders through social media marketing, including current and potential customers, current and potential employees, journalists, bloggers, and the general public. On a strategic level, social media marketing includes the management of a marketing campaign, governance, setting the scope and the establishment of a firm's desired social media "culture" and "tone".

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.

<span class="mw-page-title-main">User profile</span> Data about an individual user

A user profile is a collection of settings and information associated with a user. It contains critical information that is used to identify an individual, such as their name, age, portrait photograph and individual characteristics such as knowledge or expertise. User profiles are most commonly present on social media websites such as Facebook, Instagram, and LinkedIn; and serve as voluntary digital identity of an individual, highlighting their key features and traits. In personal computing and operating systems, user profiles serve to categorise files, settings, and documents by individual user environments, known as ‘accounts’, allowing the operating system to be more friendly and catered to the user. Physical user profiles serve as identity documents such as passports, driving licenses and legal documents that are used to identify an individual under the legal system.

An X bot, formerly known as Twitter bot, is a type of software bot that controls an X account via the X API. The social bot software may autonomously perform actions such as posting, reposting, liking, following, unfollowing, or direct messaging other accounts. The automation of X 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 big data from user-generated content on social media sites and mobile apps in order to extract actionable patterns, form conclusions about users, and act upon the information, often for the purpose of advertising to users or conducting research. The term is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to shift through vast quantities of raw ore to find the precious minerals; likewise, social media mining requires human data analysts and automated software programs to shift through massive amounts of raw social media data in order to discern patterns and trends relating to social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, and more. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs, new 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.

References

  1. ↑ "The influence of social bots". www.akademische-gesellschaft.com. Retrieved March 1, 2022.
  2. ↑ Lutz Finger (February 17, 2015). "Do Evil - The Business Of Social Media Bots". forbes.com.
  3. ↑ 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)
  4. ↑ 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.
  5. ↑ 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).
  6. ↑ 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.
  7. ↑ 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.
  8. ↑ 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.
  9. ↑ 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.
  10. ↑ "How to Find and Remove Fake Followers from Twitter and Instagram : Social Media Examiner".
  11. ↑ 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.
  12. 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.
  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. 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.
  23. ↑ Constine, Josh (June 20, 2018). "Instagram hits 1 billion monthly users, up from 800M in September". TechCrunch. Retrieved November 24, 2022.
  24. ↑ Narang, Satnam (January 1, 2019). "The evolution of Instagram porn bots". Computer Fraud & Security. 2019 (9): 20. doi:10.1016/S1361-3723(19)30099-5. ISSN   1361-3723. S2CID   204107862.
  25. 1 2 "Instagram's Growing Bot Problem". The Information. July 18, 2018. Retrieved November 24, 2022.
  26. ↑ "Instagram Promotion Service (Real Marketing) – UseViral". August 15, 2021. Retrieved November 24, 2022.
  27. ↑ Morales, Eduardo (March 8, 2022). "Instagram Bots in 2021 — Everything You Need To Know". Medium. Retrieved November 24, 2022.
  28. ↑ 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.
  29. ↑ Dang, Sheila; Paul, Katie (July 7, 2022). "Twitter says it removes over 1 million spam accounts each day". Reuters. Retrieved November 23, 2022.
  30. ↑ Reply, Huzaifa Azhar 2 months ago. "10 Best Twitter Bots You Should Follow in 2022 - TechPP". techpp.com. Retrieved November 24, 2022.{{cite web}}: CS1 maint: numeric names: authors list (link)
  31. ↑ "Twitter's automation development rules | Twitter Help". help.twitter.com. Retrieved November 24, 2022.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.