A comparative study of Bot Detection techniques methods with an application related to Covid-19 discourse on Twitter

Bot Detection is an essential asset in a period where Online Social Networks(OSN) is a part of our lives. This task becomes more relevant in crises, as the Covid-19 pandemic, where there is an incipient risk of proliferation of social bots, producing a possible source of misinformation. In order to address this issue, it has been compared different methods to detect automatically social bots on Twitter using Data Selection. The techniques utilized to elaborate the bot detection models include the utilization of features as the tweets’ metadata or the Digital Fingerprint of the Twitter accounts. In addition, it was analyzed the presence of bots in tweets from different periods of the first months of the Covid-19 pandemic, using the bot detection technique which best fits the scope of the task. Moreover, this work includes also analysis over aspects regarding the discourse of bots and humans, such as sentiment or hashtag utilization.

[1]  Vlado Keselj,et al.  Twitter Bot Detection using Diversity Measures , 2019, ICNLSP.

[2]  Maximilian Mozes,et al.  Measuring Emotions in the COVID-19 Real World Worry Dataset , 2020, NLPCOVID19.

[3]  Eni Mustafaraj,et al.  From Obscurity to Prominence in Minutes: Political Speech and Real-Time Search , 2010 .

[4]  Dan Mercea,et al.  The Brexit Botnet and User-Generated Hyperpartisan News , 2017 .

[5]  Jürgen Knauth,et al.  Language-Agnostic Twitter-Bot Detection , 2019, RANLP.

[6]  Conor Hayes,et al.  Detecting Bot Behaviour in Social Media using Digital DNA Compression , 2019, AICS.

[7]  Patric R. Spence,et al.  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 , 2014, Comput. Hum. Behav..

[8]  Filippo Menczer,et al.  The rise of social bots , 2014, Commun. ACM.

[9]  Kristina Lerman,et al.  Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set , 2020, JMIR public health and surveillance.

[10]  Yi Wang,et al.  Mental health problems and social media exposure during COVID-19 outbreak , 2020, PloS one.

[11]  Philip N. Howard,et al.  Political Bots and the Manipulation of Public Opinion in Venezuela , 2015, ArXiv.

[12]  Emilio Ferrara,et al.  Deep Neural Networks for Bot Detection , 2018, Inf. Sci..

[13]  Wiebke Wagner,et al.  Steven Bird, Ewan Klein and Edward Loper: Natural Language Processing with Python, Analyzing Text with the Natural Language Toolkit , 2010, Lang. Resour. Evaluation.

[14]  Jonas Lundberg,et al.  Towards a language independent Twitter bot detector , 2019, DHN.

[15]  Emilio Ferrara,et al.  What types of COVID-19 conspiracies are populated by Twitter bots? , 2020, First Monday.

[16]  Randall Livingstone.  Socialbots and Their Friends: Digital Media and the Automation of Sociality , 2019, Inf. Soc..

[17]  Roberto Di Pietro,et al.  The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race , 2017, WWW.

[18]  Zixing Zhang,et al.  An Agreement and Sparseness-based Learning Instance Selection and its Application to Subjective Speech Phenomena , 2014, LREC 2014.

[19]  Silvio Lattanzi,et al.  SoK: The Evolution of Sybil Defense via Social Networks , 2013, 2013 IEEE Symposium on Security and Privacy.

[20]  Emilio Ferrara,et al.  Red Bots Do It Better:Comparative Analysis of Social Bot Partisan Behavior , 2019, WWW.

[21]  Rong Zhang,et al.  Data selection for speech recognition , 2007, 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU).

[22]  Raúl Monroy,et al.  Contrast Pattern-Based Classification for Bot Detection on Twitter , 2019, IEEE Access.

[23]  A. Tanju Erdem,et al.  RANSAC-based training data selection for emotion recognition from spontaneous speech , 2010, AFFINE '10.

[24]  Mehwish Nasim,et al.  Real-time Detection of Content Polluters in Partially Observable Twitter Networks , 2018, WWW.

[25]  David W. McDonald,et al.  Dissecting a Social Botnet: Growth, Content and Influence in Twitter , 2015, CSCW.

[26]  Roberto Di Pietro,et al.  Social Fingerprinting: Detection of Spambot Groups Through DNA-Inspired Behavioral Modeling , 2017, IEEE Transactions on Dependable and Secure Computing.

[27]  Alessandro Flammini,et al.  Detection of Novel Social Bots by Ensembles of Specialized Classifiers , 2020, CIKM.

[28]  Yifei Zhang,et al.  #DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate , 2018, ICWSM.

[29]  Stefano Cresci,et al.  A decade of social bot detection , 2020, Commun. ACM.

[30]  Jon Crowcroft,et al.  Of Bots and Humans (on Twitter) , 2017, ASONAM.

[31]  Christos Faloutsos,et al.  Retweeting Activity on Twitter: Signs of Deception , 2015, PAKDD.

[32]  Filippo Menczer,et al.  Arming the public with artificial intelligence to counter social bots , 2019, Human Behavior and Emerging Technologies.

[33]  Eric Gilbert,et al.  VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text , 2014, ICWSM.

[34]  Fabrizio Lillo,et al.  Cashtag Piggybacking , 2018, ACM Trans. Web.

[35]  Filippo Menczer,et al.  Online Human-Bot Interactions: Detection, Estimation, and Characterization , 2017, ICWSM.

[36]  Jonas Kaiser,et al.  The False positive problem of automatic bot detection in social science research , 2020, PloS one.

[37]  FaloutsosChristos,et al.  Catching Synchronized Behaviors in Large Networks , 2016 .

[38]  Gerardo Chowell,et al.  A Twitter Dataset of 150+ million tweets related to COVID-19 for open research , 2020 .

[39]  Maurizio Tesconi,et al.  RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter , 2019, WebSci.

[40]  Fabrício Benevenuto,et al.  A Benchmark Comparison of State-of-the-Practice Sentiment Analysis Methods , 2015, ArXiv.

[41]  Jacob Ratkiewicz,et al.  Detecting and Tracking Political Abuse in Social Media , 2011, ICWSM.

[42]  Emilio Ferrara,et al.  Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election , 2017, First Monday.

[43]  Emilio Ferrara,et al.  Social Bots Distort the 2016 US Presidential Election Online Discussion , 2016, First Monday.

[44]  Stefan Stieglitz,et al.  Are social bots a real threat? An agent-based model of the spiral of silence to analyse the impact of manipulative actors in social networks , 2019, Eur. J. Inf. Syst..

[45]  Kyumin Lee,et al.  Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter , 2011, ICWSM.

[46]  Fabrizio Lillo,et al.  $FAKE: Evidence of Spam and Bot Activity in Stock Microblogs on Twitter , 2018, ICWSM.