Combating abuse on social media platforms using natural language processing
type: CreativeWork
description: The World Wide Web has significantly changed the way people interact with one another. With the advent of social media, the web has given humans a way to directly connect to billions of others. As with any new technology, there are great opportunities and benefits, but also dangers and risk. This thesis focuses on the many ways that bad actors can have a negative impact on social media platforms and we explore novel methods that leverage natural language processing (NLP) techniques to combat this abuse First, we analyse and detect suspended accounts on social media platforms. Platform providers suspend accounts that violate a platform's terms for a number of reasons (e.g., spam, offensive and explicit language, etc.). To understand this problem further, we perform a detailed linguistic and statistical analysis into the textual information of suspended accounts and show how insights from our study significantly improve a deep-learning-based detection framework. Since early detection of these high-risk accounts is crucial, we show that this framework can be used to detect suspended accounts earlier than the social media platform. Additionally, we investigate how suspended account detection can be further improved using domain adaptation of word embeddings. In this context we show how cybersecurity related classification can generally be improved by leveraging domain-specific and general unstructured text resources. We then apply this strategy to suspended account detection and further improve the performance of our previous models. Second, we propose novel methods to detect compromised social media accounts, which is a common way for malicious users to spread misinformation and spam. Since the adversary exploits the already established trust of a compromised account, it is crucial to detect these accounts to limit the damage they can cause. We propose a novel method for discovering compromised accounts by semantic analysis of text messages coming out from an account. …
abstract: The World Wide Web has significantly changed the way people interact with one another. With the advent of social media, the web has given humans a way to directly connect to billions of others. As with any new technology, there are great opportunities and benefits, but also dangers and risk. This thesis focuses on the many ways that bad actors can have a negative impact on social media platforms and we explore novel methods that leverage natural language processing (NLP) techniques to combat this abuse First, we analyse and detect suspended accounts on social media platforms. Platform providers suspend accounts that violate a platform's terms for a number of reasons (e.g., spam, offensive and explicit language, etc.). To understand this problem further, we perform a detailed linguistic and statistical analysis into the textual information of suspended accounts and show how insights from our study significantly improve a deep-learning-based detection framework. Since early detection of these high-risk accounts is crucial, we show that this framework can be used to detect suspended accounts earlier than the social media platform. Additionally, we investigate how suspended account detection can be further improved using domain adaptation of word embeddings. In this context we show how cybersecurity related classification can generally be improved by leveraging domain-specific and general unstructured text resources. We then apply this strategy to suspended account detection and further improve the performance of our previous models. Second, we propose novel methods to detect compromised social media accounts, which is a common way for malicious users to spread misinformation and spam. Since the adversary exploits the already established trust of a compromised account, it is crucial to detect these accounts to limit the damage they can cause. We propose a novel method for discovering compromised accounts by semantic analysis of text messages coming out from an account. …
author: Seyler, Dominic
language: en
keywords: Natural Language Processing, Social Media, Machine Learning, Cybersecurity
date published: 2022-01-12T21:45:39Z
date modified: 2026-07-22T22:24:52Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/113025