Papers Twitter Bot Detection

BotUmc: An Uncertainty-Aware Twitter Bot Detection with Multi-view Causal Inference

Social bots have become widely known by users of social platforms. To prevent social bots from spreading harmful speech, many novel bot detections are proposed. However, with the evolution of social bots, detection metho…

Iteration over event space in time-to-first-spike spiking neural networks for Twitter bot classification

This study proposes a framework that extends existing time-coding time-to-first-spike spiking neural network (SNN) models to allow processing information changing over time. We explain spike propagation through a model w…

LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection

As malicious actors employ increasingly advanced and widespread bots to disseminate misinformation and manipulate public opinion, the detection of Twitter bots has become a crucial task. Though graph-based Twitter bot de…

BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipeline

Twitter, as one of the most popular social networks, provides a platform for communication and online discourse. Unfortunately, it has also become a target for bots and fake accounts, resulting in the spread of false inf…

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

Accurate bot detection is necessary for the safety and integrity of online platforms. It is also crucial for research on the influence of bots in elections, the spread of misinformation, and financial market manipulation…

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

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have …

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

Twitter bots are automatic programs operated by malicious actors to manipulate public opinion and spread misinformation. Research efforts have been made to automatically identify bots based on texts and networks on socia…

TwiBot-22: Towards Graph-Based Twitter Bot Detection

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

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

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 …

State of the Art Models for Fake News Detection Tasks

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. The proposed solution…

Twitter Bot Detection Using Bidirectional Long Short-term Memory Neural Networks and Word Embeddings

Twitter is a web application playing dual roles of online social networking and micro-blogging. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots. Legitimate …

Detecting Bot Behaviour in Social Media using Digital DNA Compression

A major challenge faced by online social networks such as Facebook and Twitter is the remarkable rise of fake and automated bot accounts over the last few years. Some of these accounts have been reported to engage in und…

Twitter Bot Detection using Diversity Measures

Language-Agnostic Twitter-Bot Detection

In this paper we address the problem of detecting Twitter bots. We analyze a dataset of 8385 Twitter accounts and their tweets consisting of both humans and different kinds of bots. We use this data to train machine lear…

Bot and Gender Detection of Twitter Accounts Using Distortion and LSA

In this work, we present our approach for the Author Profiling task of PAN 2019. The task is divided into two sub-problems, bot, and gender detection, for two different languages: English and Spanish. For each instance…

Evading classifiers in discrete domains with provable optimality guarantees

Machine-learning models for security-critical applications such as bot, malware, or spam detection, operate in constrained discrete domains. These applications would benefit from having provable guarantees against advers…