A Twitter bot is a type of bot software that controls a Twitter account via the Twitter API. The bot software may autonomously perform actions such as tweeting, re-tweeting, 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, or spamming.
It is sometimes desirable to identify when a Twitter account is controlled by a bot. In a 2012 paper, 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 Twitter bots as a credible source of information.
There are many different types of Twitter bots and their purposes vary from one to another. Some bots may tweet helpful material such as @EarthquakesSF (description below). In total, Twitter bots are estimated to create approximately 24% of tweets that are on Twitter. Here are examples of some of the Twitter bots and how they interact with users on Twitter.
@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.
@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, most likely a victim of its own success.
@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.
@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.
@everyword has tweeted every word of the English language. It started in 2007 and tweeted every thirty minutes until 2014.
@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.
@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.
@infinite_scream tweets and auto-replies a 2-39 character scream. At least partially inspired by Edvard Munch's The Scream, it attracted attention from those distressed by the Presidency of Donald Trump and bad news.
@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.
@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.
@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.
There are also families of related Twitter bots. For example, @LessicoFeed, @SpracheFeed, @SwedishFeed, @TraductionFeed, @VocabularioFeed, @WelshFeed each tweet an English word along with a translation every hour into Italian, German, Swedish, French, Spanish, and Welsh, respectively. The translations are crowdsourced by volunteers and subscribers.
Detecting non-human Twitter users has been of interest to academics. Indiana University has developed a free service called Botometer (formerly BotOrNot), which scores Twitter handles based on their likelihood of being a Twitterbot. One significant academic study estimated that up to 15% of Twitter users were automated bot accounts. 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.
A subset of Twitter bots programmed to complete social tasks played an important role in the United States 2016 Presidential Election. 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. Concerns about political Twitter bots include the promulgation of malicious content, increased polarization, and the spreading of fake news.
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. @tinycarebot is a Twitterbot that encourages followers to practice self care, and brands are increasingly using automated Twitter bots to engage with customers in interactive ways. One anti-bullying organization has created @TheNiceBot, which attempts to combat the prevalence of mean tweets by automatically tweeting kind messages.
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. 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. 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. Account sellers may charge a premium for more realistic accounts that have Twitter profile pictures and bios and retweet the accounts they follow. In addition to an ego boost, public figures may gain more lucrative endorsement contracts from inflated Twitter metrics. 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.
- 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. Retrieved 1 August 2014.
- "Automation rules". Twitter Help Center. Retrieved 2017-04-22.
- Martin Bryant (August 11, 2009). "12 weird and wonderful Twitter Retweet Bots". TNW. Retrieved August 1, 2014.
- Protalinski, Emil (2013-03-08). "Dear Assistant: A Twitter bot that uses Wolfram Alpha to answer your burning questions". The Next Web, Inc. Retrieved 1 August 2014.
- David Daw (October 23, 2011). "10 Twitter Bot Services to Simplify Your Life". PCWorld. Retrieved May 31, 2012.
- "Twitter spam is out of control". The Verge. 2016-08-30. Retrieved 2017-04-22.
- 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.
- 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.CS1 maint: Multiple names: authors list (link)
- Cashmore, Pete. "Twitter Zombies: 24% of Tweets Created by Bots". Retrieved 19 March 2014.
- Christine Erickson (July 22, 2012). "Don't Block These 10 Hilarious Twitter Bots". Mashable. Retrieved December 28, 2012.
- Mosendz, Polly (2014-07-24). "Congressional IP Address Blocked from Making Edits to Wikipedia". Retrieved 1 August 2014.
- "The 8 best Twitter bots you aren't following". Digital Trends. 2013-08-02. Retrieved 2016-05-24.
- Bonnie Burton (4 March 2016). "Drumpf Twitterbot learns to imitate Trump via deep-learning algorithm". CNET. CBS Interactive. Retrieved 4 March 2016.
- Judah, Sam; Ajala, Hannah (3 August 2015). "The Twitter bot that 'corrects' people who say 'illegal immigrant'". BBC News. Retrieved 3 August 2015.
- Dubbin, Rob (2013-11-14). "The Rise of Twitter Bots". The New Yorker. Retrieved 9 March 2014.
- Farrier, John. "Twitter Bot Pranks Gullible People with Hilariously Fake Facts". NeatoCMS. Retrieved 16 March 2014.
- "Twitter Suspends @fuckeveryword for Tweeting 'Fuck N*****s'". Gizmodo UK. December 21, 2017. Retrieved January 1, 2019.
- Adrian Chen (23 February 2012). "How I Found the Human Being Behind Horse_ebooks, The Internet's Favorite Spambot". Gawker. Retrieved 4 May 2012.
- Reed, Nora. "Cheap Bots, Done Quick!". cheapbotsdonequick.com.
- 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.
- 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.
- 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.
- 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.
- "This Self-Care Bot Makes Twitter a Healthier Place". Time. Retrieved 2017-03-12.
- "We need your help!! Help us improve our translations. :)". reddit.com. Retrieved 2018-09-30.
- Dewangan, Madhuri (2016). SocialBot: Behavioral Analysis & Detection. International Symposium on Security in Computing and Communication. Communications in Computer and Information Science. 625. pp. 450–460. doi:10.1007/978-981-10-2738-3_39. ISBN 978-981-10-2737-6.
- Davis, Clayton A.; Onur Varol; Emilio Ferrara; Alessandro Flammini; Filippo Menczer (2016). "BotOrNot: A System to Evaluate Social Bots". Proc. WWW Developers Day Workshop. doi:10.1145/2872518.2889302.
- 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 – via dl.acm.org.
- arXiv, Emerging Technology from the. "How to Spot a Social Bot on Twitter".
- 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).
- Hill, Kashmir. "The Invasion of the Twitter Bots".
- "This Twitter bot tricks angry trolls into arguing with it for hours". 7 October 2016.
- Collins, Ben (15 June 2016). "A Twitter Bot Is Beating Trump Fans". The Daily Beast – via www.thedailybeast.com.
- Pareene, Alex. "How We Fooled Donald Trump Into Retweeting Benito Mussolini".
- "ૐ൬ҽժɨƈɨռɛ ฬටℓғ on Twitter". 2016-05-31.
- McGill, Andrew (2 June 2016). "Have Twitter Bots Infiltrated the 2016 Election?".
- "Um, Did Kellyanne Conway Just Tweet a Hidden Neo-Nazi Message To a White Nationalist?". 14 February 2017.
- Bessi, Alessandro; Ferrara, Emilio (3 November 2016). "Social bots distort the 2016 U.S. Presidential election online discussion". First Monday. 21 (11) – via firstmonday.org.
- 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: 4787. doi:10.1038/s41467-018-06930-7.
- "As Twitter moves to purge fake accounts, conservatives say they are being targeted - The Boston Globe".
- "The best Twitter bots of 2015". Quartz. Retrieved 2018-05-01.
- "12 Weird, Excellent Twitter Bots Chosen by Twitter's Best Bot-Makers". 2015-11-09.
- "50 Innovative Ways Brands Use Chatbots - TOPBOTS". 20 October 2016.
- "This Self-Care Bot Makes Twitter a Healthier Place". Time.
- "Anti-bullying bot built to say nice things to 300 million people on Twitter". Telegraph.co.uk. Retrieved 2017-04-13.
- "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.
- Perlroth, Nicole. "Researchers Call Out Twitter Celebrities With Suspicious Followings". Bits Blog. Retrieved 2017-04-13.
- Perlroth, Nicole. "Fake Twitter Followers Become Multimillion-Dollar Business". Bits Blog. Retrieved 2017-04-13.
- "Buzzkill: Coca-Cola Finds No Sales Lift from Online Chatter". Retrieved 2017-04-18.
- "Coca-Cola Says Social Media Buzz Does Not Boost Sales". Retrieved 2017-04-18.