Twitter Bot Detection

9 papers with code • 2 benchmarks • 3 datasets

Academic studies estimate that up to 15% of Twitter users are automated bot accounts [1]. 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. Hence, detecting non-human Twitter users or automated bot accounts using machine learning techniques has become an area of interest to researchers in the last few years.


Most implemented papers

Evading classifiers in discrete domains with provable optimality guarantees

spring-epfl/trickster 25 Oct 2018

We introduce a graphical framework that (1) generalizes existing attacks in discrete domains, (2) can accommodate complex cost functions beyond $p$-norms, including financial cost incurred when attacking a classifier, and (3) efficiently produces valid adversarial examples with guarantees of minimal adversarial cost.

Bot and Gender Detection of Twitter Accounts Using Distortion and LSA

andreabac3/Bot-Gender-Profiling-Pan2019 1 Jul 2019

In this work, we present our approach for the Author Profiling task of PAN 2019.

Detecting Bot Behaviour in Social Media using Digital DNA Compression

pasricha/bot-dna-compression 27th Irish Conference on Artificial Intelligence and Cognitive Science, 2019 2019

In our approach, we employ a lossless compression algorithm on these Digital DNA sequences and use the compression statistics as a measure of predictability in the behaviour of a group of Twitter accounts.

State of the Art Models for Fake News Detection Tasks

aub-mind/fake-news-detection 11 May 2020

This paper presents state of the art methods for addressing three important challenges in automated fake news detection: fake news detection, domain identification, and bot identification in tweets.

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

alexdrk14/usbotdetection 8 Dec 2021

Twitter is one of the most popular social networks attracting millions of users, while a considerable proportion of online discourse is captured.

TwiBot-22: Towards Graph-Based Twitter Bot Detection

luoundergradxjtu/twibot-22 9 Jun 2022

Twitter bot detection has become an increasingly important task to combat misinformation, facilitate social media moderation, and preserve the integrity of the online discourse.

BIC: Twitter Bot Detection with Text-Graph Interaction and Semantic Consistency

LzyFischer/BIC 17 Aug 2022

In addition, given the stealing behavior of novel Twitter bots, BIC proposes to model semantic consistency in tweets based on attention weights while using it to augment the decision process.

MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark

graphdetec/mgtab 3 Jan 2023

However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research.

Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection

johnchrishays/bot-detection 17 Jan 2023

These tools employ machine learning and often achieve near perfect performance for classification on existing datasets, suggesting bot detection is accurate, reliable and fit for use in downstream applications.