scholarly journals GANBOT: a GAN-based framework for social bot detection

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Shaghayegh Najari ◽  
Mostafa Salehi ◽  
Reza Farahbakhsh
Keyword(s):  
Bot Detection

DCA for Bot Detection

2008 ◽  
Author(s):  
Yousof Al-Hammadi ◽  
Uwe Aickelin ◽  
Julie Greensmith
Keyword(s):  
Bot Detection

scholarly journals Bot Datasets on Twitter: Analysis and Challenges

2021 ◽  
Vol 11 (9) ◽  
pp. 4105
Author(s):  
Luis Daniel Samper-Escalante ◽  
Octavio Loyola-González ◽  
Raúl Monroy ◽  
Miguel Angel Medina-Pérez
Keyword(s):  
Information Exchange ◽  
Modern Society ◽  
Online News ◽  
Social Networking Service ◽  
Public And Private ◽  
Challenges And Opportunities ◽  
Twitter Analysis ◽  
Radar Chart ◽  
New Challenges ◽  
Bot Detection

The reach and influence of social networks over modern society and its functioning have created new challenges and opportunities to prevent the misuse or tampering of such powerful tools of social interaction. Twitter, a social networking service that specializes in online news and information exchange involving billions of users world-wide, has been infested by bots for several years. In this paper, we analyze both public and private databases from the literature of bot detection on Twitter. We summarize their advantages, disadvantages, and differences, recommending which is more suitable to work with depending on the necessities of the researcher. From this analysis, we present five distinct behaviors in automated accounts exhibited across all the bot datasets analyzed from these databases. We measure their level of presence in each dataset using a radar chart for visual comparison. Finally, we identify four challenges that researchers of bot detection on Twitter have to face when using these databases from the literature.

scholarly journals A decade of social bot detection

2020 ◽  
Vol 63 (10) ◽  
pp. 72-83 ◽  
Author(s):  
Stefano Cresci
Keyword(s):  
Bot Detection

scholarly journals Game Bot Detection Based on Avatar Trajectory

2008 ◽  
pp. 94-105 ◽  
Author(s):  
Kuan-Ta Chen ◽  
Andrew Liao ◽  
Hsing-Kuo Kenneth Pao ◽  
Hao-Hua Chu
Keyword(s):  
Bot Detection

Feature Engineering for Social Bot Detection

2018 ◽  
pp. 311-334 ◽  
Author(s):  
Onur Varol ◽  
Clayton A. Davis ◽  
Filippo Menczer ◽  
Alessandro Flammini
Keyword(s):  
Feature Engineering ◽  
Bot Detection

scholarly journals Real-Time Bot Detection from Twitter Using the Twitterbot+ Framework

2020 ◽  
Vol 26 (4) ◽  
pp. 496-507
Author(s):  
Kheir Daouadi ◽  
Rim Rebaï ◽  
Ikram Amous
Keyword(s):  
Real Time ◽  
Time Use ◽  
Detection System ◽  
Twitter Data ◽  
Benchmark Datasets ◽  
Filtering Technique ◽  
Bot Detection ◽  
Independent Features ◽  
Quality Of Research

Nowadays, bot detection from Twitter attracts the attention of several researchers around the world. Different bot detection approaches have been proposed as a result of these research efforts. Four of the main challenges faced in this context are the diversity of types of content propagated throughout Twitter, the problem inherent to the text, the lack of sufficient labeled datasets and the fact that the current bot detection approaches are not sufficient to detect bot activities accurately. We propose, Twitterbot+, a bot detection system that leveraged a minimal number of language-independent features extracted from one single tweet with temporal enrichment of a previously labeled datasets. We conducted experiments on three benchmark datasets with standard evaluation scenarios, and the achieved results demonstrate the efficiency of Twitterbot+ against the state-of-the-art. This yielded a promising accuracy results (>95%). Our proposition is suitable for accurate and real-time use in a Twitter data collection step as an initial filtering technique to improve the quality of research data.

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