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1	Argument Mining: Claim Annotation, Identification, Verification Karamolegkou, Antonia January 2021 (has links) Researchers writing scientific articles summarize their work in the abstracts mentioning the final outcome of their study. Argumentation mining can be used to extract the claim of the researchers as well as the evidence that could support their claim. The rapid growth of scientific articles demands automated tools that could help in the detection and evaluation of the scientific claims’ veracity. However, there are neither a lot of studies focusing on claim identification and verification neither a lot of annotated corpora available to effectively train deep learning models. For this reason, we annotated two argument mining corpora and perform several experiments with state-of-the-art BERT-based models aiming to identify and verify scientific claims. We find that using SciBERT provides optimal results regardless of the dataset. Furthermore, increasing the amount of training data can improve the performance of every model we used. These findings highlight the need for large-scale argument mining corpora, as well as domain-specific pre-trained models. machine learning nlp argument mining transformers BERT
2	UNDERSTANDING AND ANALYZING MICROTARGETING PATTERN ON SOCIAL MEDIA Tunazzina Islam (20738480) 18 February 2025 (has links) <p dir="ltr">We now live in a world where we can reach people directly through social media, without relying on traditional media such as television and radio. The landscape of social media is highly distributed, as users generate and consume a variety of content. On the other hand, social media platforms collect vast amounts of data and create very specific profiles of different users through targeted advertising. Various interest groups, politicians, advertisers, and stakeholders utilize these platforms to target potential users to advance their interests by adapting their messaging. A significant challenge lies in understanding this messaging and how it changes depending on the targeted user groups. Another challenge arises when we do not know who the users are and what their motivations are for engaging with content. The initial phase of our research focuses on comprehensively understanding users and their underlying motivations, whether practitioner-based or promotional. Gaining this understanding is crucial in reshaping our perspective on the content disseminated by these users. Step beyond that, assuming the identification of the involved parties, this study aims to characterize the messaging and explore how it adapts based on various targeted demographic groups. This thesis addresses these challenges by developing computational approaches and frameworks for (1) characterizing user types and their motivations, (2) analyzing the messaging based on topics relevant to the users and their responses to it, and (3) delving into the deeper understanding of the themes and arguments involved in the messaging.</p> Natural language processing Computational Social Science Social Media Mining Large Language Models LLMs Social Media Text Analysis Social Media Analysis microtargeting social media campaigns Weak Supervision Relational Learning Bayesian Model Averaging method graph embedding model Machine-in-the-Loop Human-in-the-Loop LLMs-in-the-Loop Argument mining Moral Foundation Theory COVID-19 Vaccine Campaign Climate Campaign Characterizing Messaging Characterizing Users Vaccine Debate Political Campaign social media ads Social Media Yoga Keto Diet Joint Embedding Model Understanding Microtargeting Pattern Analyzing Microtargeting Ad Transparency Neuro Symbolic Model uncover themes uncover talking points uncover arguments

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Argument Mining: Claim Annotation, Identification, Verification

UNDERSTANDING AND ANALYZING MICROTARGETING PATTERN ON SOCIAL MEDIA