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A Twitter bot is a type of software bot that controls a Twitter account via the Twitter API.[1] The social bot software may autonomously perform actions such as tweeting, re-tweeting, liking, following, unfollowing, or direct messaging other accounts.[2] The automation of Twitter accounts is governed by a [3]set of automation rules that outline proper and improper uses of automation.[4] Proper usage includes broadcasting helpful information, automatically generating interesting or creative content, and automatically replying to users via direct message.[5][6][7] Improper usage includes circumventing API rate limits, violating user privacy, spamming,[8] and sockpuppeting. Twitter bots may be part of a larger botnet. They can be used to influence elections and in misinformation campaigns.

Twitter's policies do allow non-abusive bots, such as those created as a benign hobby or for artistic purposes,[9] or posting helpful information.[10]

Types

Positive influence

Many non-malicious bots are popular for their entertainment value. However, as technology and the creativity of bot-makers improves, so does the potential for Twitter bots that fill social needs.[11][12] @tinycarebot is a Twitter bot that encourages followers to practice self care, and brands are increasingly using automated Twitter bots to engage with customers in interactive ways.[13][14] One anti-bullying organization has created @TheNiceBot, which attempts to combat the prevalence of mean tweets by automatically tweeting kind messages.[15]

Political

Further information: Russian interference in the 2016 United States elections

Concerns about political Twitter bots include the promulgation of malicious content, increased polarization, and the spreading of fake news.[16][17][18] A subset of Twitter bots programmed to complete social tasks played an important role in the United States 2016 Presidential Election.[19] Researchers estimated that pro-Trump bots generated four tweets for every pro-Clinton automated account and out-tweeted pro-Clinton bots 7:1 on relevant hashtags during the final debate. Deceiving Twitter bots fooled candidates and campaign staffers into retweeting misappropriated quotes and accounts affiliated with incendiary ideals.[20][21][22] Twitter bots have also been documented to influence online politics in Venezuela.[23] In 2019, 20% of the global Twitter trends were found to be created automatically using bots originating from Turkey. It is reported that 108,000 bot accounts were bulk tweeting to push 19,000 keywords to top trends in Turkey, to promote slogans such as political campaigns related to the 2019 Turkish local elections.[24]

In November 2022, Chinese bots coordinately flood Twitter with garbage information (e.g. online gambling ads) so as to distract the users' attention away from the protests.[25] These bots, disguised as attractive girls, hashtag the major cities in China.[26]

Fake followers

The majority of Twitter accounts following public figures and brands are often fake or inactive, making the number of Twitter followers a celebrity has a difficult metric for gauging popularity.[27] While this cannot always be helped, some public figures who have gained or lost huge quantities of followers in short periods of time have been accused of discreetly paying for Twitter followers.[28][29] For example, the Twitter accounts of Sean Combs, Rep Jared Polis (D-Colo), PepsiCo, Mercedes-Benz, and 50 Cent have come under scrutiny for possibly engaging in the buying and selling of Twitter followers, which is estimated to be between a $40 million and $360 million business annually.[28][29] Account sellers may charge a premium for more realistic accounts that have Twitter profile pictures and bios and retweet the accounts they follow.[29] In addition to an ego boost, public figures may gain more lucrative endorsement contracts from inflated Twitter metrics.[28] For brands, however, the translation of online buzz and social media followers into sales has recently come under question after The Coca-Cola Company disclosed that a corporate study revealed that social media buzz does not create a spike in short term sales.[30][31]

Identification

It is sometimes desirable to identify when a Twitter account is controlled by a internet bot.[32] Following a test period, Twitter rolled out labels to identify bot accounts and automated tweets in February 2022.[33][34]

Detecting non-human Twitter users has been of interest to academics.[32][35]

In a 2012 paper,[1] Chu et al. propose the following criteria that indicate that an account may be a bot (they were designing an automated system):

  • "Periodic and regular timing" of tweets;
  • Whether the tweet content contains known spam; and
  • The ratio of tweets from mobile versus desktop, as compared to an average human Twitter user.

Emilio Ferrara at the University of Southern California used artificial intelligence to identify Twitter bots. He found that humans reply to other tweets four or five times more than bots and that bots continue to post longer tweets over time.[36] Bots also post at more regular time gaps, for example, tweeting at 30-minute or 60-minute intervals.[36]

Indiana University has developed a free service called Botometer[37] (formerly BotOrNot), which scores Twitter handles based on their likelihood of being a Twitterbot.[38][39][40]

Recent research from EPFL argued that classifying a Twitter account as bot or not may not be always possible because hackers take over human accounts and use them as bots temporarily or permanently[41] and in parallel to the owner of the account in some cases.[24]

See also: Author profiling § Author profiling and the Internet

Examples

There are many different types of Twitter bots and their purposes vary from one to another. Some examples include:

  • @Betelgeuse_3 sends at-replies in response to tweets that include the phrase, "Beetlejuice, beetlejuice, beetlejuice". The tweets are sent in the voice of the lead character from the Beetlejuice film.[42]
  • @CongressEdits and @parliamentedits posts whenever someone makes edits to Wikipedia from the United States Congress and United Kingdom Parliament IP addresses, respectively.[43] @CongressEdits was suspended in 2018 while @parliamentedits is still running.
  • @DBZNappa replied with "WHAT!? NINE THOUSAND?" to anyone on Twitter that used the internet meme phrase "over 9000." The account began in 2011, and was eventually suspended in 2015.[44]
  • @DearAssistant sends auto-reply tweets responding to complex queries in simple English by utilizing Wolfram Alpha.[6]
  • @DeepDrumpf is a recurrent neural network, created at MIT, that releases tweets imitating Donald Trump's speech patterns. It received its namesake from the term 'Donald Drumpf', popularized in the segment 'Donald Trump' from the show Last Week Tonight with John Oliver.[45]
  • @DroptheIBot tweets the message, "People aren't illegal. Try saying 'undocumented immigrant' or 'unauthorized immigrant' instead" to Twitter users who have sent a tweet containing the phrase "illegal immigrant". It was created by American Fusion.net journalists Jorge Rivas and Patrick Hogan.[46]
  • @everyword has tweeted every word of the English language. It started in 2007 and tweeted every thirty minutes until 2014.[47]
  • @nyt_first_said tweets every time The New York Times uses a word for the first time. It was created by artist and engineer Max Bittker in 2017.[48][49]
  • @factbot1 was created by Eric Drass to illustrate what he believed to be a prevalent problem: that of people on the internet believing unsupported facts which accompany pictures.[50]
  • @fuckeveryword was tweeting every word in the English language preceded by "fuck", but Twitter suspended it midway through operation because the account tweeted "fuck niggers".[51] @fckeveryword was created after the suspension to resurrect the task, which it completed in 2020.[52]
  • @Horse ebooks was a bot that gained a following among people who found its tweets poetic. It has inspired various _ebooks-suffixed Twitter bots which use Markov text generators (or similar techniques) to create new tweets by mashing up the tweets of their owner.[53] It went inactive following a brief promotion for Bear Stearns Bravo.
  • @infinite_scream tweets and auto-replies a 2–39 character scream.[54] At least partially inspired by Edvard Munch's The Scream,[55] it attracted attention from those distressed by the Presidency of Donald Trump[56] and bad news.[55]
  • @MetaphorMagnet is an AI bot that generates metaphorical insights using its knowledge-base of stereotypical properties and norms. A companion bot @MetaphorMirror pairs these metaphors to news tweets. Another companion bot, @BestOfBotWorlds, uses metaphor to generate faux-religious insights.[57]
  • @Pentametron finds tweets incidentally written in iambic pentameter using the CMU Pronouncing Dictionary, pairs them into couplets using a rhyming dictionary, and retweets them as couplets into followers' feeds.[58]
  • @RedScareBot tweets in the persona of Joseph McCarthy in response to Twitter posts mentioning "socialist", "communist", or "communism".[42]
  • @tinycarebot promotes simple self care actions to its followers, such as remembering to look up from your screens, taking a break to go outside, and drink more water. It will also send a self care suggestion if you tweet directly at it.[59]
  • @DisinfoNews Disinformation News Aggregator automatically retweets tweets that shares news articles or scientific work related to disinformation, bots or trolls from experts relevant to those topics.[60]

Prevalence

In 2009, based on a study by Sysomos, Twitter bots were estimated to create approximately 24% of tweets on Twitter.[61] According to the company, there were 20 million, fewer than 5%, of accounts on Twitter that were fraudulent in 2013.[62] In 2013, two Italian researchers calculated 10 percent of total accounts on Twitter were "bots" although other estimates have placed the figure even higher.[63] One significant academic study in 2017 estimated that up to 15% of Twitter users were automated bot accounts.[64][65] A 2020 estimate puts the figure at 15% of all accounts or around 48 million accounts.[66]

A 2023 MIT study found that third-party tools used to detect bots may not be as accurate as they are trained on data being collected in simplistic ways, and each tweet in these training sets then manually labeled by people as a bot or a human.[67] Already in 2019 German researchers scrutinized studies that were using Botswatch and Botometer, dismissing them as fundamentally flawed and concluded that (unlike spam accounts) there is no evidence that "social bots“ even exist.[68]

Impact

The prevalence of Twitter bots coupled with the ability of some bots to give seemingly human responses has enabled these non-human accounts to garner widespread influence.[69][70][21][71] The social implications these Twitter bots potentially have on human perception are sizeable according to a study published by the ScienceDirect Journal. Looking at the Computers as Social Actors (CASA) paradigm, the journal notes, "people exhibit remarkable social reactions to computers and other media, treating them as if they were real people or real places." The study concluded that Twitter bots were viewed as credible and competent in communication and interaction making them suitable for transmitting information in the social media sphere.[72] Whether posts are perceived to be generated by humans or bots depends on partisanship, a 2023 study found.[73]

See also

References

  1. ^ a b Chu, Zi; Gianvecchio, Steven; Wang, Haining; Jajodia, Sushil (2012). "Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg?" (PDF). IEEE Transactions on Dependable and Secure Computing. 9 (6): 811–824. doi:10.1109/TDSC.2012.75. ISSN 1545-5971. S2CID 351844. Archived from the original (PDF) on 28 March 2018. Retrieved 1 August 2014.
  2. ^ Uttam, Ankur (2019-08-02). "Ankur Uttam". Authors group. doi:10.1287/ee25ecbf-2e8b-4a02-b9c6-c7fb0396fe69. S2CID 240598332. Retrieved 2023-07-14.
  3. ^ Uttam, Ankur (2019-08-02). "Ankur Uttam". Authors group. doi:10.1287/ee25ecbf-2e8b-4a02-b9c6-c7fb0396fe69. S2CID 240598332. Retrieved 2023-07-14.
  4. ^ "Automation rules". Twitter Help Center. Archived from the original on 2017-12-05. Retrieved 2017-04-22.
  5. ^ Martin Bryant (August 11, 2009). "12 weird and wonderful Twitter Retweet Bots". TNW. Archived from the original on August 10, 2018. Retrieved August 1, 2014.
  6. ^ a b Protalinski, Emil (2013-03-08). "Dear Assistant: A Twitter bot that uses Wolfram Alpha to answer your burning questions". The Next Web, Inc. Archived from the original on 2019-04-20. Retrieved 1 August 2014.
  7. ^ David Daw (October 23, 2011). "10 Twitter Bot Services to Simplify Your Life". PCWorld. Archived from the original on November 13, 2017. Retrieved May 31, 2012.
  8. ^ "Twitter spam is out of control". The Verge. 2016-08-30. Archived from the original on 2018-07-31. Retrieved 2017-04-22.
  9. ^ "Platform manipulation and spam policy". April 2022. Archived from the original on 2022-05-31. Retrieved 2022-05-28.
  10. ^ Automation rules, 3 Nov 2017, archived from the original on 5 December 2017, retrieved 28 May 2022
  11. ^ "The best Twitter bots of 2015". Quartz. Archived from the original on 2019-01-14. Retrieved 2018-05-01.
  12. ^ "12 Weird, Excellent Twitter Bots Chosen by Twitter's Best Bot-Makers". 2015-11-09. Archived from the original on 2018-09-22. Retrieved 2020-02-21.
  13. ^ "50 Innovative Ways Brands Use Chatbots - TOPBOTS". 20 October 2016. Archived from the original on 25 April 2019. Retrieved 18 April 2017.
  14. ^ "This Self-Care Bot Makes Twitter a Healthier Place". Time. Archived from the original on 2018-10-05. Retrieved 2017-03-12.
  15. ^ "Anti-bullying bot built to say nice things to 300 million people on Twitter". Telegraph.co.uk. Archived from the original on 2018-06-26. Retrieved 2017-04-13.
  16. ^ Bessi, Alessandro; Ferrara, Emilio (3 November 2016). "Social bots distort the 2016 U.S. Presidential election online discussion". First Monday. 21 (11). doi:10.5210/fm.v21i11.7090. S2CID 20990413. Archived from the original on 5 October 2018. Retrieved 18 April 2017 – via firstmonday.org.
  17. ^ Shao, Chengcheng; Giovanni Luca Ciampaglia; Onur Varol; Kaicheng Yang; Alessandro Flammini; Filippo Menczer (2018). "The spread of low-credibility content by social bots". Nature Communications. 9 (1): 4787. arXiv:1707.07592. Bibcode:2018NatCo...9.4787S. doi:10.1038/s41467-018-06930-7. PMC 6246561. PMID 30459415.
  18. ^ "As Twitter moves to purge fake accounts, conservatives say they are being targeted - The Boston Globe". The Boston Globe. Archived from the original on 2018-07-09. Retrieved 2018-04-04.
  19. ^ McGill, Andrew (2 June 2016). "Have Twitter Bots Infiltrated the 2016 Election?". The Atlantic. Archived from the original on 20 February 2019. Retrieved 18 April 2017.
  20. ^ "Archived copy" (PDF). Archived from the original (PDF) on 2016-11-09. Retrieved 2017-04-18.{{cite web}}: CS1 maint: archived copy as title (link)
  21. ^ a b Pareene, Alex (28 February 2016). "How We Fooled Donald Trump Into Retweeting Benito Mussolini". Archived from the original on 2016-06-27. Retrieved 2017-04-18.
  22. ^ "Um, Did Kellyanne Conway Just Tweet a Hidden Neo-Nazi Message To a White Nationalist?". The Daily Banter. 14 February 2017. Archived from the original on 17 May 2017. Retrieved 18 April 2017.
  23. ^ Morales, Juan S. (2020). "Perceived Popularity and Online Political Dissent: Evidence from Twitter in Venezuela". The International Journal of Press/Politics. 25: 5–27. doi:10.1177/1940161219872942. S2CID 203053725.
  24. ^ a b Elmas, Tuğrulcan; Overdorf, Rebekah; Özkalay, Ahmed Furkan; Aberer, Karl (2021). "Ephemeral Astroturfing Attacks: The Case of Fake Twitter Trends". 6th IEEE European Symposium on Security and Privacy. Virtual: IEEE. arXiv:1910.07783.
  25. ^ Davidson, Helen; Milmo, Dan (28 November 2022). "Chinese bots flood Twitter in attempt to obscure Covid protests". TheGuardian.com. Archived from the original on 28 November 2022. Retrieved 28 November 2022.
  26. ^ BRZESKI, PATRICK; RAHMAN, ABID (28 November 2022). "Chinese Bots Inundate Twitter With Pornographic Spam Amid COVID Protests". The Hollywood Reporter. Archived from the original on 28 November 2022. Retrieved 28 November 2022.
  27. ^ "Justin Bieber, Katy Perry, Rihanna, Taylor Swift and Lady Gaga: Who's faking it on Twitter?". Music Business Worldwide. 2015-01-31. Archived from the original on 2019-04-21. Retrieved 2017-04-13.
  28. ^ a b c Perlroth, Nicole (25 April 2013). "Researchers Call Out Twitter Celebrities With Suspicious Followings". Bits Blog. Archived from the original on 2018-11-09. Retrieved 2017-04-13.
  29. ^ a b c Perlroth, Nicole (5 April 2013). "Fake Twitter Followers Become Multimillion-Dollar Business". Bits Blog. Archived from the original on 2018-12-21. Retrieved 2017-04-13.
  30. ^ "Buzzkill: Coca-Cola Finds No Sales Lift from Online Chatter". Archived from the original on 2019-04-22. Retrieved 2017-04-18.
  31. ^ "Coca-Cola Says Social Media Buzz Does Not Boost Sales". Archived from the original on 2019-04-21. Retrieved 2017-04-18.
  32. ^ a b Ferrara, Emilio; Varol, Onur; Davis, Clayton; Menczer, Filippo; Flammini, Alessandro (2015). "The Rise of Social Bots". Communications of the ACM. 59 (7): 96–104. arXiv:1407.5225. doi:10.1145/2818717. S2CID 1914124. Archived from the original on 2017-10-18. Retrieved 2018-07-19.
  33. ^ Espósito, Filipe (2021-09-09). "Twitter testing new labels to identify 'Good Bots' accounts and tweets". 9to5Mac. Archived from the original on 2022-09-27. Retrieved 2022-05-23.
  34. ^ Perez, Sarah (2022-02-17). "Twitter officially launches labels to identify the 'good bots'". TechCrunch. Retrieved 2022-05-23.
  35. ^ Dewangan, Madhuri (2016). "SocialBot: Behavioral Analysis and Detection". Security in Computing and Communications. Communications in Computer and Information Science. Vol. 625. pp. 450–460. doi:10.1007/978-981-10-2738-3_39. ISBN 978-981-10-2737-6.
  36. ^ a b Lu, Donna (2 May 2020). "AI can root out bots on Twitter". New Scientist. 246 (3280): 17. Bibcode:2020NewSc.246...17L. doi:10.1016/S0262-4079(20)30851-4. S2CID 219071467. Archived from the original on 14 May 2022. Retrieved 14 May 2022.
  37. ^ "Botometer". Archived from the original on 2020-05-26. Retrieved 2018-07-19.
  38. ^ 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.
  39. ^ Chu, Zi; Gianvecchio, Steven; Wang, Haining; Jajodia, Sushil (6 December 2010). "Who is tweeting on Twitter". Who is tweeting on Twitter: human, bot, or cyborg?. ACM. pp. 21–30. doi:10.1145/1920261.1920265. ISBN 9781450301336. S2CID 6494787 – via dl.acm.org.
  40. ^ arXiv, Emerging Technology from the. "How to Spot a Social Bot on Twitter". Archived from the original on 2020-02-19. Retrieved 2017-04-18.
  41. ^ Elmas, Tuğrulcan; Overdorf, Rebekah; Aberer, Karl (2022). "Characterizing Retweet Bots: The Case of Black Market Accounts". Proceedings of the International AAAI Conference on Web and Social Media. 16. Atlanta, Georgia: AAAI: 171–182. arXiv:2112.02366. doi:10.1609/icwsm.v16i1.19282. S2CID 244908788.
  42. ^ a b Christine Erickson (July 22, 2012). "Don't Block These 10 Hilarious Twitter Bots". Mashable. Archived from the original on November 18, 2018. Retrieved December 28, 2012.
  43. ^ Mosendz, Polly (2014-07-24). "Congressional IP Address Blocked from Making Edits to Wikipedia". Archived from the original on 2016-03-28. Retrieved 1 August 2014.
  44. ^ "The 8 best Twitter bots you aren't following". Digital Trends. 2013-08-02. Archived from the original on 2016-05-10. Retrieved 2016-05-24.
  45. ^ Bonnie Burton (4 March 2016). "Drumpf Twitterbot learns to imitate Trump via deep-learning algorithm". CNET. CBS Interactive. Archived from the original on 16 March 2019. Retrieved 4 March 2016.
  46. ^ Judah, Sam; Ajala, Hannah (3 August 2015). "The Twitter bot that 'corrects' people who say 'illegal immigrant'". BBC News. Archived from the original on 13 February 2019. Retrieved 3 August 2015.
  47. ^ Dubbin, Rob (2013-11-14). "The Rise of Twitter Bots". The New Yorker. Archived from the original on 2014-07-01. Retrieved 9 March 2014.
  48. ^ Symonds, Alexandria (2019-07-07). "When The Times First Says It, This Twitter Bot Tracks It". The New York Times. ISSN 0362-4331. Archived from the original on 2023-03-10. Retrieved 2023-03-10.
  49. ^ "Do You Speak New York Times?". The New Yorker. 2023-03-07. Archived from the original on 2023-03-10. Retrieved 2023-03-10.
  50. ^ Farrier, John. "Twitter Bot Pranks Gullible People with Hilariously Fake Facts". NeatoCMS. Archived from the original on 17 May 2018. Retrieved 16 March 2014.
  51. ^ "The bot that tweeted "fuck" in front of every word was doomed from the start". Archived from the original on 2021-09-17. Retrieved 2021-09-17.
  52. ^ "Fuck Every Word 2.0". Twitter. Archived from the original on 2022-03-15. Retrieved 2022-03-15.
  53. ^ Adrian Chen (23 February 2012). "How I Found the Human Being Behind Horse_ebooks, The Internet's Favorite Spambot". Gawker. Archived from the original on 17 April 2013. Retrieved 4 May 2012.
  54. ^ Reed, Nora. "Cheap Bots, Done Quick!". cheapbotsdonequick.com. Archived from the original on 2017-10-03. Retrieved 2017-03-30.
  55. ^ a b Adkins, Ariel (26 February 2017). "This Twitter Account Reacts To The Bad News In Your Timeline With an Infinite Scream". observer.com. New York Observer. Archived from the original on 27 February 2017.
  56. ^ Grant, Megan (February 2017). "15 Totally Legit Ways To Deal When All You Want To Do Is Scream". bustle.com. Bustle. Archived from the original on 30 March 2017.
  57. ^ Veale, Tony (2015). Game of Tropes: Exploring the Placebo Effect in Computational Creativity (PDF). ICCC-2015: Proceedings of the Sixth International Conference on Computational Creativity. Park City, Utah. Archived from the original (PDF) on 2015-08-13. Retrieved 2015-10-17.
  58. ^ Max Read (30 April 2012). "Weird Internets: The Amazing Found-on-Twitter Sonnets of Pentametron". Gawker. Archived from the original on March 21, 2014. Retrieved 9 March 2016.
  59. ^ "This Self-Care Bot Makes Twitter a Healthier Place". Time. Archived from the original on 2018-10-05. Retrieved 2017-03-12.
  60. ^ "DisinfoNews". Archived from the original on 2022-12-06. Retrieved 2023-02-02.
  61. ^ Cashmore, Pete (6 August 2009). "Twitter Zombies: 24% of Tweets Created by Bots". Mashable. Archived from the original on 6 September 2018. Retrieved 19 March 2014.
  62. ^ D'onfro, Jillian (October 4, 2013). "Twitter Admits 5% Of Its 'Users' Are Fake". Business Insider. Archived from the original on March 1, 2021. Retrieved May 15, 2014.
  63. ^ Woollacott, Emma. "Why fake Twitter accounts are a political problem". New Statesman. Archived from the original on February 25, 2021. Retrieved June 16, 2014.
  64. ^ 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). Archived from the original on 2018-08-28. Retrieved 2018-07-19.
  65. ^ Hill, Kashmir. "The Invasion of the Twitter Bots". Forbes. Archived from the original on 2019-02-12. Retrieved 2017-04-18.
  66. ^ Rodrıguez-Ruiz, Jorge; Mata-Sanchez, Javier Israel; Monroy, Raul; Loyola-Gonzalez, Octavio; Ĺopez-Cuevas, Armando (April 2020). "A one-class classification approach for bot detection on Twitter". Computers & Security. 91: 101715. doi:10.1016/j.cose.2020.101715. S2CID 212689495. Archived from the original on 17 June 2022. Retrieved 17 June 2022.
  67. ^ https://mitsloan.mit.edu/ideas-made-to-matter/study-finds-bot-detection-software-isnt-accurate-it-seems
  68. ^ https://background.tagesspiegel.de/digitalisierung/the-social-bot-fairy-tale
  69. ^ actually, this source does not seem to support neither the claim of "prevalence" nor the "widespread" influence; Jay Hathaway merely portrays one amusing example of a troll-baiting tool: "This Twitter bot tricks angry trolls into arguing with it for hours". The Daily Dot. 7 October 2016. Archived from the original on 19 October 2018. Retrieved 18 April 2017.
  70. ^ Collins, Ben (15 June 2016). "A Twitter Bot Is Beating Trump Fans". The Daily Beast. Archived from the original on 2 August 2020. Retrieved 8 July 2018 – via www.thedailybeast.com.
  71. ^ K.A. 42Σ [@5thdimdreamz] (31 May 2016). "@andrewmcgill 👽 perhaps 😏" (Tweet). Archived from the original on 25 May 2021. Retrieved 8 April 2022 – via Twitter.{{cite web}}: CS1 maint: numeric names: authors list (link)
  72. ^ Spence, P.R.; Shelton, Ashleigh; Edwards, Chad; Edwards, Autumn (2013). "Is that a bot running the social media feed? Testing the differences in perceptions of communication quality for a human agent and a bot agent on Twitter". Computers in Human Behavior. 33: 372–376. doi:10.1016/j.chb.2013.08.013.
  73. ^ https://insight.kellogg.northwestern.edu/article/is-there-a-bot-behind-that-tweet
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