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Twitter bot

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A Twitterbot is a type of bot software that controls a Twitter account via the Twitter API.[1] The 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.[2] Proper usage includes broadcasting helpful information, automatically generating interesting or creative content, and automatically replying to users via direct message.[3][4][5] Improper usage includes circumventing API rate limits, violating user privacy, or spamming.[6]

Features

It is sometimes desirable to identify when a Twitter account is controlled by a bot. 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.

Research shows that humans can view Twitterbots as a credible source of information.[7]

Examples

This is a dynamic list and may never be able to satisfy particular standards for completeness. You can help by adding missing items with reliable sources.

There are many different types of Twitterbots and their purposes vary from one to another. Some bots may tweet helpful material such as @EarthquakesSF (description below). In total, Twitterbots are estimated to create approximately 24% of tweets that are on Twitter.[8] Here are examples of some of the Twitterbots and how they interact with users on Twitter.

@assbott is a bot that tweeted Donald Trump and was often the first reply. When replied to, Assbott tweets random phrases at people leading some to believe he was making a conversation with them.

@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, mostly likely a victim of its own success.[9]

@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.[10]

@choose_this sends at-replies to Twitter users who tweet about making a choice between a wide variety of things.[11]

@CongressEdits and @parliamentedits posts whenever someone makes edits to Wikipedia from the US Congress and UK Parliament IP addresses, respectively.[12]

@DearAssistant sends auto-reply tweets responding to complex queries in simple English by utilizing Wolfram Alpha.[4]

@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.[13]

@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.[14]

@EarthquakesSF tweets about earthquakes in the San Francisco Bay Area as they happen using real-time seismographic information from the USGS.[15]

@everyword has tweeted every word of the English language. It started in 2008 and tweeted every thirty minutes until 2014.[16]

@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.[17]

@Horse ebooks is a bot that has 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.[18]

@infinite_scream tweets and auto-replies a 2-39 character scream.[19] At least partially inspired by Edvard Munch's The Scream,[20] it attracted attention from those distressed by the Presidency of Donald Trump[21] and bad news.[20]

@KookyScrit sends auto-reply tweets correcting misspellings of the word "weird".[22]

@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.[23]

@oppositePOTUS automatically tweets the opposite of what @realDonaldTrump tweets. It was created by Justin Hook, who also made thet viral game Google Feud.[24]

@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.[25]

@RedScareBot tweets in the persona of Joseph McCarthy in response to Twitter posts mentioning "socialist", "communist", or "communism".[10]

@Tauntbot replies to anyone who mentions it with a randomly generated, verbose insult. It also periodically tweets random taunts at nobody in particular.[26]

@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.[27]

@Wikipediafinds "tweets dozens of things that could have their own Wikipedia article if our consciousness could catalogue them", according to Paste Magazine.[28] It is run by Ayako S, a mysterious girl and former lover of Dylan Anaya, who ran @Wikifinds (which was banned by Twitter and Wikipedia.) She claims to live alone under constant surveillance by the police somewhere in South Korea.[citation needed]

@ne_wiki_recent tweets Nepali wikipedia recent changes, with a link to the page and name of the editor.

Impact

Detecting non-human Twitter users has been of interests to academics. Indiana University has developed a BotOrNot free service, which scores Twitter handles based on their likelihood of being a Twitterbot.[29][30][31] One significant academic study estimated that up to 15% of Twitter users were accounts automated bots.[32][33] 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.[34][35]

Political

A subset of Twitter Bots programmed to complete social tasks played an important role in the United States 2016 Presidential Election.[36] 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.[37][38][39] Concerns about political Twitter Bots include the promulgation of malicious content, increased polarization, and the spreading of fake news.[40]

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 Twitterbots that fill social needs.[41][42] @tinycarebot is a Twitterbot that encourages followers to practice self care, and brands are increasingly using automated Twitterbots to engage with customers in interactive ways.[43][44] One anti-bullying organization has created @TheNiceBot, which attempts to combat the prevalence of mean tweets by automatically tweeting kind messages.[45]

Public Figures

The majority of Twitter accounts following public figures and brands are often fake or inactive, making the number of Twitter followers a celebrity a difficult metric for gauging popularity.[46] 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.[47][48] 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.[47][48] Account sellers may charge a premium for more realistic accounts that have Twitter profile pictures and bios and retweet the accounts they follow.[48] In addition to an ego boost, public figures may gain more lucrative endorsement contracts from inflated Twitter metrics.[47] 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.[49][50]

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). IEEE. doi:10.1109/TDSC.2012.75. ISSN 1545-5971. Retrieved 1 August 2014.
  2. ^ "Automation rules". Twitter Help Center. Retrieved 2017-04-22.
  3. ^ Martin Bryant (August 11, 2009). "12 weird and wonderful Twitter Retweet Bots". TNW. Retrieved August 1, 2014.
  4. ^ a b Protalinski, Emil. "Dear Assistant: A Twitter bot that uses Wolfram Alpha to answer your burning questions". The Next Web, Inc. Retrieved 1 August 2014.
  5. ^ David Daw (October 23, 2011). "10 Twitter Bot Services to Simplify Your Life". PCWorld. Retrieved May 31, 2012.
  6. ^ "Twitter spam is out of control". The Verge. 2016-08-30. Retrieved 2017-04-22.
  7. ^ 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". Elsevier. 33: 372–376. doi:10.1016/j.chb.2013.08.013.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  8. ^ Cashmore, Pete. "Twitter Zombies: 24% of Tweets Created by Bots". Retrieved 19 March 2014.
  9. ^ "The 8 best Twitter bots you aren't following". Digital Trends. 2013-08-02. Retrieved 2016-05-24.
  10. ^ a b Christine Erickson (July 22, 2012). "Don't Block These 10 Hilarious Twitter Bots". Mashable. Retrieved December 28, 2012.
  11. ^ Gallagher, Brenden. "The 25 Most Ridiculous Twitterbots". ComplexTech. Retrieved 16 March 2014.
  12. ^ Mosendz, Polly. "Congressional IP Address Blocked from Making Edits to Wikipedia". Retrieved 1 August 2014.
  13. ^ Bonnie Burton (4 March 2016). "Drumpf Twitterbot learns to imitate Trump via deep-learning algorithm". CNET. CBS Interactive. Retrieved 4 March 2016.
  14. ^ Judah, Sam; Ajala, Hannah (3 August 2015). "The Twitter bot that 'corrects' people who say 'illegal immigrant'". BBC Online. Retrieved 3 August 2015.
  15. ^ "100 Best Earthquake Twitter Bots".
  16. ^ Dubbin, Rob. "The Rise of Twitter Bots". The New Yorker. Retrieved 9 March 2014.
  17. ^ Farrier, John. "Twitter Bot Pranks Gullible People with Hilariously Fake Facts". NeatoCMS. Retrieved 16 March 2014.
  18. ^ Adrian Chen (23 February 2012). "How I Found the Human Being Behind Horse_ebooks, The Internet's Favorite Spambot". Gawker. Retrieved 4 May 2012.
  19. ^ Reed, Nora. "Cheap Bots, Done Quick!". cheapbotsdonequick.com.
  20. ^ 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.
  21. ^ Grant, Megan. "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.
  22. ^ "Rise of the Twitterbot: A Modern Language App for Good and Evil". Listen & Learn. Retrieved 1 August 2014.
  23. ^ 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.
  24. ^ "I made a bot that tries to tweet the opposite of whatever @realDonaldTrump does. It's like 95% garbage but so is he. Follow @oppositePOTUS!". Twitter. Retrieved 15 August 2017.
  25. ^ 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.
  26. ^ "This is @tauntbot, a Twitter bot that will mercilessly insult you". Fusion. Retrieved 2015-10-22.
  27. ^ "This Self-Care Bot Makes Twitter a Healthier Place". Time. Retrieved 2017-03-12.
  28. ^ "10 Weird Twitter Beings Worth a Follow". pastemagazine.com.
  29. ^ http://dl.acm.org/citation.cfm?id=1920265
  30. ^ https://www.technologyreview.com/s/529461/how-to-spot-a-social-bot-on-twitter/
  31. ^ http://truthy.indiana.edu/botornot/
  32. ^ https://arxiv.org/pdf/1703.03107.pdf
  33. ^ https://www.forbes.com/sites/kashmirhill/2012/08/09/the-invasion-of-the-twitter-bots/#3325d1551c31
  34. ^ https://www.dailydot.com/unclick/arguebot-twitter-bot-bait-jerks/ http://www.thedailybeast.com/articles/2016/06/15/a-twitter-bot-is-beating-trump-fans.html http://gawker.com/how-we-fooled-donald-trump-into-retweeting-benito-musso-1761795039
  35. ^ https://twitter.com/5thdimdreamz/status/737609961610448900
  36. ^ https://www.theatlantic.com/politics/archive/2016/06/have-twitter-bots-infiltrated-the-2016-election/484964/
  37. ^ http://politicalbots.org/wp-content/uploads/2016/10/Data-Memo-Third-Presidential-Debate.pdf
  38. ^ http://gawker.com/how-we-fooled-donald-trump-into-retweeting-benito-musso-1761795039
  39. ^ http://thedailybanter.com/2017/02/kellyanne-conway-nationalist-tweet/
  40. ^ http://firstmonday.org/ojs/index.php/fm/article/view/7090/5653
  41. ^ https://qz.com/572763/the-best-twitter-bots-of-2015/
  42. ^ http://nymag.com/selectall/2015/11/12-weirdest-funniest-smartest-twitter-bots.html
  43. ^ http://www.topbots.com/50-innovative-ways-brands-use-chatbots/
  44. ^ http://time.com/4573201/tiny-care-bot-self-care-twitter/
  45. ^ "Anti-bullying bot built to say nice things to 300 million people on Twitter". Telegraph.co.uk. Retrieved 2017-04-13.
  46. ^ "Justin Bieber, Katy Perry, Rihanna, Taylor Swift and Lady Gaga: Who's faking it on Twitter?". Music Business Worldwide. 2015-01-31. Retrieved 2017-04-13.
  47. ^ a b c Perlroth, Nicole. "Researchers Call Out Twitter Celebrities With Suspicious Followings". Bits Blog. Retrieved 2017-04-13.
  48. ^ a b c Perlroth, Nicole. "Fake Twitter Followers Become Multimillion-Dollar Business". Bits Blog. Retrieved 2017-04-13.
  49. ^ "Buzzkill: Coca-Cola Finds No Sales Lift from Online Chatter". Retrieved 2017-04-18.
  50. ^ "Coca-Cola Says Social Media Buzz Does Not Boost Sales". Retrieved 2017-04-18.

Literature

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