Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection [PDF]
The Web Conference, 2023 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.
Chris Hays +4 more
semanticscholar +1 more source
IEEE Internet of Things Journal, 2021 Identification of anomaly and malicious traffic in the Internet-of-Things (IoT) network is essential for the IoT security to keep eyes and block unwanted traffic flows in the IoT network.
M. Shafiq +4 more
semanticscholar +1 more source
Online Human-Bot Interactions: Detection, Estimation, and Characterization [PDF]
International Conference on Web and Social Media, 2017 Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter.
Onur Varol +4 more
semanticscholar +1 more source
Bot or Human? Detecting ChatGPT Imposters with A Single Question [PDF]
arXiv.org, 2023 Large language models (LLMs) like GPT-4 have recently demonstrated impressive capabilities in natural language understanding and generation. However, there is a concern that they can be misused for malicious purposes, such as fraud or denial-of-service ...
Hong Wang +3 more
semanticscholar +1 more source
Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection [PDF]
The Web Conference, 2023 The presence of a large number of bots in Online Social Networks (OSN) leads to undesirable social effects. Graph neural networks (GNNs) are effective in detecting bots as they utilize user interactions.
S. Shi +5 more
semanticscholar +1 more source
Social bot detection in the age of ChatGPT: Challenges and opportunities
First Monday, 2023 We present a comprehensive overview of the challenges and opportunities in social bot detection in the context of the rise of sophisticated AI-based chatbots.
Emilio Ferrara
semanticscholar +1 more source
LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection [PDF]
Web Search and Data Mining, 2023 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.
Zijian Cai +6 more
semanticscholar +1 more source
BotRGCN: Twitter bot detection with relational graph convolutional networks [PDF]
International Conference on Advances in Social Networks Analysis and Mining, 2021 Twitter bot detection is an important and challenging task. Existing bot detection measures fail to address the challenge of community and disguise, falling short of detecting bots that disguise as genuine users and attack collectively.
Shangbin Feng +3 more
semanticscholar +1 more source
Machine learning-based social media bot detection: a comprehensive literature review
Social Network Analysis and Mining, 2023 In today’s digitalized era, Online Social Networking platforms are growing to be a vital aspect of each individual’s daily life. The availability of the vast amount of information and their open nature attracts the interest of cybercriminals to create ...
M. Aljabri +5 more
semanticscholar +1 more source
Collecting Vulnerable Source Code from Open-Source Repositories for Dataset Generation
Applied Sciences, 2020 Different Machine Learning techniques to detect software vulnerabilities have emerged in scientific and industrial scenarios. Different actors in these scenarios aim to develop algorithms for predicting security threats without requiring human ...
Razvan Raducu +3 more
doaj +1 more source