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Bots among us prevalence, influence, and roles of automated accounts in the German Twitter follow network

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Social bots are undermining trust in social media. They spread low-credibility content (Shao et al., 2018), so-called fake news (Vosoughi, Roy, & Aral, 2018), and spam (Bruns et al., 2018). However, most research analyses data based on the active sharing of links, keywords, or hashtags rather than assessing the longer-term presence of bots as an integral part of platforms.
To address this gap, we present what to our knowledge is the first study that assesses the prevalence, influence, and roles of automated accounts in a Twitter follow network on a national scale: the German-speaking Twittersphere. This work in progress allows us to analyse the long-term structural role, impact, and possible audience of bots beyond the context of single events and topics.

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Bots among us prevalence, influence, and roles of automated accounts in the German Twitter follow network

  1. 1. Background
  2. 2. Step 1: Drawing a Sample
  3. 3. Keyword extraction with chi-squared criterion active accounts keywords top accounts tag 2015 e3, stream, xd, e32019, nintendo, twitch, game, crossing, pc, zelda, animal, gameplay, cyberpunk2077, games, switch, xbox, trailer, cyberpunk, gaming, xboxe3, uff, nice, awesome, keanu, pk, live, nen, lol, mega unge, dagibee, Gronkh, MelinaSophie, LeFloid, iBlali, Taddl, rewinside, HandIOfIBlood, PietSmiet YouTubers & Gaming 1855 berlin, innen (female suffix), spd (German party), berliner, study, companies, discuss, cdu (party), demand, topics, important, federal government, german, digitisation, topic, has been, annefrank, climate protection, more, june, shows, germany, interview, politics, brandenburg tazgezwitscher, Die_Gruenen, Tagesspiegel, c_lindner, gutjahr, dunjahayali, sigmargabriel, sixtus, HeikoMaas, spdde German politics 1414 women’s strike, switzerland, swiss people, glarner, svp (Swiss party), bern, women’s strike2019, zurich, canton, grand, basel, national council, women NZZ, 20min, viktorgiacobbo, Blickch, tagesanzeiger, srfnews, MikeMuellerLate, watson_news, migros, srf3 Swiss politics / women's strike 1044 season, trainer, bundesliga, new arrival, player, dfb, fc, gerest, em, exchanges, estonia, exchange, transfer, goal, victory, team, wm, cup, liga DFB_Team, FCBayern, ToniKroos, MarioGoetze, esmuellert_, Podolski10, Manuel_Neuer, Bundesliga_DE, ZDFsport, JB17Official German football 767 övp (Austrian party), spö (Austrian party), fpö (Austrian party), vienna, austria, bierlein, oenr, austria‘s, ibiza, viennese, strache, kickl, turquoise, hofer, parliament, national council, zib2, abdullah, mandate, election campaign, centre, chancellor (female form), heinz, austrian, glyphosate, proposal, blue florianklenk, sebastiankurz, IngridThurnher, kesslermichael, HannoSettele, vanderbellen, Gawhary, HBrandstaetter, HHumorlos, michelreimon Austrian politics 376 dessau, roßlau, afd (German (far-)right-wing party), görlitz, islam, raped, migrants, asylum seeker, dangerous person, rejected person, sed, wippel, patriots, greta, niger, hosni, strongest, green, fridayforfuture, african, rosslau, left, gretathunberg, crime, old parties, merkel, radical left, greens, habeck, keep silent, tear apart, vote, girl, refugee, saxony, rape, maas, citizen, islamistic, sexual DonJoschi, AfD, MSF_austria, Alice_Weidel, SteinbachErika, Joerg_Meuthen, Beatrix_vStorch, GrumpyMerkel, krone_at hard right / xenophobia / migration / refugees
  4. 4. Step 3: Rate suspected Bots with a human rater
  5. 5. Example Accounts Bots posting images / quotes
  6. 6. Example Accounts Follow Back Bots
  7. 7. Example Accounts Content Linking / Own Website Linking
  8. 8. Spam / Possibly Malign
  9. 9. Conclusion ● Prevalence of (suspected) bot accounts in line with prior analyses 1 ● Low centrality of automated accounts (usually outside central clusters) ● Bots are primarily located outside of politics and news clusters ● Botometer: false-positive rate higher than selected threshold (63% vs. 75% set threshold) according to human ratings ● But: good approach to inductively explore bot-categories and test bot-detection methods Overall, the negative impact of automated accounts seems rather low in our sample. 1 Varol, O., Ferrara, E., Davis, C. A., Menczer, F., & Flammini, A. (2017). Online Human-Bot Interactions: Detection, Estimation, and Characterization. In International AAAI Conference on Web and Social Media. Retrieved from https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15587/14817

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