Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study

  title={Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study},
  author={Alexander Shevtsov and Christos Tzagkarakis and Despoina Antonakaki and Sotiris Ioannidis},
Twitter is one of the most popular social networks attracting millions of users, while a considerable proportion of online discourse is captured. It provides a simple usage framework with short messages and an efficient application programming interface (API) enabling the research community to study and analyze several aspects of this social network. However, the Twitter usage simplicity can lead to malicious handling by various bots. The malicious handling phenomenon expands in online… 

Machine Learning Algorithms for Detecting and Analyzing Social Bots Using a Novel Dataset

A new benchmark is initiated on a 1.5M Twitter profile to detect bots on Twitter and various autofeature selections are utilized to identify the most influential features and remove the less influential ones, improving the model performance by at least 2% by applying over-/undersampling.

Social Network Users Profiling Using Machine Learning for Information Security Tasks

The aim of the work is to develop methods for detecting bots using machine learning and intelligent analysis, and uses gradient boosting with an accuracy of AUC = 0.9999 to solve the problem of determining whether a user account is genuine or a bot is hiding behind it.



Using sentiment to detect bots on Twitter: Are humans more opinionated than bots?

By analyzing a large dataset relating to the 2014 Indian election, it is shown that a number of sentimentrelated factors are key to the identification of bots, significantly increasing the Area under the ROC Curve (AUROC).

Contrast Pattern-Based Classification for Bot Detection on Twitter

A pattern-based classification mechanism is used to social bot detection, specifically for Twitter, and a new feature model is introduced, which extends (part of) an existing model with features out of Twitter account usage and tweet content sentiment analysis.

Predicting susceptibility to social bots on Twitter

It is found that a users' Klout score, friends count, and followers count are most predictive of whether a user will interact with a bot, and that the Random Forest algorithm produces the best classifier, when used in conjunction with one of the better feature ranking algorithms.

Buzzer Detection and Sentiment Analysis for Predicting Presidential Election Results in a Twitter Nation

The study suggests that Twitter can serve as an important resource for any political activity, specifically for predicting the final outcomes of the election itself and leveraged the number of positive sub-tweets for each candidate.

Detecting Bots on Russian Political Twitter

A methodology for detecting bots on Twitter using an ensemble of classifiers is developed and applied to study bot activity within political discussions in the Russian Twittersphere, finding suggestive evidence that one prominent activity that bots were involved in on Russian political Twitter is the spread of news stories and promotion of media who produce them.

Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making

It is demonstrated how the results of the classifier can be robustly utilized in a statistical model used to forecast the likely spread of cyber hate in a sample of Twitter data.

Online Human-Bot Interactions: Detection, Estimation, and Characterization

This work presents a framework to detect social bots on Twitter, and describes several subclasses of accounts, including spammers, self promoters, and accounts that post content from connected applications.

Who is tweeting on Twitter: human, bot, or cyborg?

This paper proposes a classification system that uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot or cyborg on Twitter and demonstrates the efficacy of the proposed classification system.

Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign

  • Adam BadawyEmilio FerraraKristina Lerman
  • Computer Science
    2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
  • 2018
Although an ideologically broad swath of Twitter users were exposed to Russian trolls in the period leading up to the 2016 U.S. Presidential election, it was mainly conservatives who helped amplify their message, revealing that they had a mostly conservative, pro-Trump agenda.

Evolution of bot and human behavior during elections

It is shown that, in the 2018 midterms, bots changed the volume and the temporal dynamics of their online activity to better mimic humans and avoid detection.