Public opinion of military organizations significantly influences their ability to recruit talented individuals. As recruitment efforts increasingly extend into digital spaces like social media, it becomes essential to assess the stance of social media users toward online military content. However, there is a notable lack of data for analyzing opinions on military recruiting efforts online, compounded by challenges in stance labeling, which is crucial for understanding public perceptions. Despite the importance of stance analysis for successful online military recruitment, creating human-annotated, in-domain stance labels is resource-intensive. In this paper, we address both the challenges of stance labeling and the scarcity of data on public opinions of online military recruitment by introducing and releasing the DIVERSE dataset: https://doi.org/10.5281/zenodo.10493803. This dataset comprises all comments from the U.S. Army's official YouTube Channel videos. We employed a state-of-the-art weak supervision approach, leveraging large language models to label the stance of each comment toward its respective video and the U.S. Army. Our findings indicate that the U.S. Army's videos began attracting a significant number of comments post-2021, with the stance distribution generally balanced among supportive, oppositional, and neutral comments, with a slight skew towards oppositional versus supportive comments.
Social media polarization during conflict: Insights from an ideological stance dataset on Israel-Palestine Reddit comments
Department of Business and Technology Management, Islamic University of Technology, Gazipur, Dhaka, Bangladesh（伊斯兰科技大学商学院与技术管理系，加兹ipur，达卡，孟加拉国）；Department of Computer Science and Engineering, Islamic University of Technology, Gazipur, Dhaka, Bangladesh（伊斯兰科技大学计算机科学与工程系，加兹ipur，达卡，孟加拉国）；Department of Electrical and Electronic Engineering, University of Dhaka, Nilkhet Road, Dhaka, Bangladesh（达卡大学电子与电气工程系，Nilkhet Road，达卡，孟加拉国）；Department of Computer Science, American International University–Bangladesh, Kuratoli, Dhaka, Bangladesh（孟加拉国美国国际大学计算机科学系，Kuratoli，达卡，孟加拉国）
PRISM of Opinions: A Persona-Reasoned Multimodal Framework for User-centric Conversational Stance Detection
Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学（深圳））；Peng Cheng Laboratory, Shenzhen, China（鹏城实验室）；Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies（广东省新型安全智能技术重点实验室）；The Hong Kong Polytechnic University, Hong Kong, China（香港理工大学）；Macau University of Science and Technology, Hong Kong, China（澳门科学技术大学）
Birla Institute of Technology and Science, Pilani（皮拉尼贝拉理工与科学学院）
Online social networks are ubiquitous and user-friendly. Nevertheless, it is vital to detect and moderate offensive content to maintain decency and empathy. However, mining social media texts is a complex task since users don't adhere to any fixed patterns. Comments can be written in any combination of languages and many of them may be low-resource. In this paper, we present our system for the LT-EDI shared task on detecting homophobia and transphobia in social media comments. We experiment with a number of monolingual and multilingual transformer based models such as mBERT along with a data a…
YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles
Purdue University（普渡大学）
每月有10亿观众，数百万用户讨论和分享意见，YouTube视频下面的评论是意见挖掘和情感分析的丰富数据来源。我们介绍了YouTube AV 50K数据集，这是一个免费提供的集合，包含超过50，000条YouTube评论和自动驾驶汽车（AV）相关视频的元数据。我们描述了它的创建过程，它的内容和数据格式，并讨论了它的可能用途。特别是，我们对第一起自动驾驶汽车死亡事故进行了案例研究，以评估数据集，并展示如何使用该数据集更好地了解公众对自动驾驶汽车的态度和公众对事故的反应。对数据集的未来发展也进行了讨论。
National University of Computer and Emerging Sciences（国家计算机与新兴科学大学）
Sentiment analysis is a vast area in the Machine learning domain. A lot of work is done on datasets and their analysis of the English Language. In Pakistan, a huge amount of data is in roman Urdu language, it is scattered all over the social sites including Twitter, YouTube, Facebook and similar applications. In this study the focus domain of dataset gathering is YouTube comments. The Dataset contains the comments of people over different Pakistani dramas and TV shows. The Dataset contains multi-class classification that is grouped The comments into positive, negative and neutral sentiment. In…
Breaking Event Rumor Detection via Stance-Separated Multi-Agent Debate
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
IndRegBias: A Dataset for Studying Indian Regional Biases in English and Code-Mixed Social Media Comments
Department of Data Science and Engineering, Indian Institute of Science Education and Research（数据科学与工程系，印度科学教育与研究学院）；Oracle Industries AI, Oracle Corporation（Oracle人工智能部，Oracle公司）
University of Wolverhampton（伍尔弗汉普顿大学）；Rochester Institute of Technology（罗切斯特理工学院）
This paper describes the WLV-RIT entry to the Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC) shared task 2020. The HASOC 2020 organizers provided participants with annotated datasets containing social media posts of code-mixed in Dravidian languages (Malayalam-English and Tamil-English). We participated in task 1: Offensive comment identification in Code-mixed Malayalam Youtube comments. In our methodology, we take advantage of available English data by applying cross-lingual contextual word embeddings and transfer learning to make predictions to Malayalam …
University of Zurich（苏黎世大学）；University of Edinburgh（爱丁堡大学）
We extract a large-scale stance detection dataset from comments written by candidates of elections in Switzerland. The dataset consists of German, French and Italian text, allowing for a cross-lingual evaluation of stance detection. It contains 67 000 comments on more than 150 political issues (targets). Unlike stance detection models that have specific target issues, we use the dataset to train a single model on all the issues. To make learning across targets possible, we prepend to each instance a natural question that represents the target (e.g. "Do you support X?"). Baseline results from m…
When Models Disagree: Rethinking LLM Evaluation for Public Comment Analysis
AI Lab, Princeton University Engineering and Applied Sciences（普林斯顿大学人工智能实验室、工程与应用科学学院）；RAND Corporation（RAND公司）；Engineering and Applied Sciences（工程与应用科学）；Science, Technology, and International Affairs（科学、技术与国际事务）；Georgetown University（乔治·华盛顿大学）
Georgia Institute of Technology（佐治亚理工学院）；University of Washington（华盛顿大学）
Dialogue models trained on human conversations inadvertently learn to generate toxic responses. In addition to producing explicitly offensive utterances, these models can also implicitly insult a group or individual by aligning themselves with an offensive statement. To better understand the dynamics of contextually offensive language, we investigate the stance of dialogue model responses in offensive Reddit conversations. Specifically, we create ToxiChat, a crowd-annotated dataset of 2,000 Reddit threads and model responses labeled with offensive language and stance. Our analysis reveals that…
Mapping Toxic Comments Across Demographics: A Dataset from German Public Broadcasting
Freie Universität Berlin（柏林自由大学）；Fraunhofer FOKUS（弗劳恩霍夫研究所（FOKUS））；MSB Medical School Berlin（柏林医学学校（MSB））；funk - Content-Netzwerk ARD / ZDF（ARD/ZDF内容网络）；InfAI Leipzig（莱比锡InfAI）
CineXDrama: Relevance Detection and Sentiment Analysis of Bangla YouTube Comments on Movie-Drama using Transformers: Insights from Interpretability Tool
Ahsanullah University Of Science and Technology (AUST)（阿萨努拉科技大学（AUST））
TikStance: A Multimodal and Hierarchical Dataset for Multi-target Stance Analysis in TikTok Political Conversations
School of Artificial Intelligence, Shenzhen Technology University（深圳技术大学人工智能学院）；School of Cyber Science and Technology, University of Science and Technology of China（中国科学技术大学网络空间安全学院）
Supporters and Skeptics: LLM-based Analysis of Engagement with Mental Health (Mis)Information Content on Video-sharing Platforms
Georgia Institute of Technology（佐治亚理工学院）；Rowan University（罗文大学）；Zucker Hillside Hospital, Psychiatry Research（扎克山坡医院精神病学研究部）；Hofstra University（霍夫斯特拉大学）
Offensive Language detection in social media platforms has been an active field of research over the past years. In non-native English spoken countries, social media users mostly use a code-mixed form of text in their posts/comments. This poses several challenges in the offensive content identification tasks, and considering the low resources available for Tamil, the task becomes much harder. The current study presents extensive experiments using multiple deep learning, and transfer learning models to detect offensive content on YouTube. We propose a novel and flexible approach of selective tr…
University of Groningen（格罗宁根大学）；Università degli Studi di Torino（都灵大学）；IT University of Copenhagen（哥本哈根信息技术大学）
As a contribution to personality detection in languages other than English, we rely on distant supervision to create Personal-ITY, a novel corpus of YouTube comments in Italian, where authors are labelled with personality traits. The traits are derived from one of the mainstream personality theories in psychology research, named MBTI. Using personality prediction experiments, we (i) study the task of personality prediction in itself on our corpus as well as on TwiSty, a Twitter dataset also annotated with MBTI labels; (ii) carry out an extensive, in-depth analysis of the features used by the c…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；W.E. Upjohn Institute（W.E.厄普约翰研究所）
In this paper we introduce a framework for annotating a social media text corpora for various categories. Since, social media data is generated via individuals, it is important to annotate the text for the individuals demographic attributes to enable a socio-technical analysis of the corpora. Furthermore, when analyzing a large data-set we can often annotate a small sample of data and then train a prediction model using this sample to annotate the full data for the relevant categories. We use a case study of a Facebook comment corpora on student loan discussion which was annotated for gender, …
From Keywords to Clusters: AI-Driven Analysis of YouTube Comments to Reveal Election Issue Salience in 2024
Georgia Tech-Europe, Sam Nunn School of International Affairs（佐治亚理工学院欧洲分校萨姆·纳恩国际事务学院）；University of Missouri Institute for Data Science and Informatics（密苏里大学数据科学与信息学研究所）；University of Missouri School of Medicine（密苏里大学医学院）；University of Missouri（密苏里大学）
Theedhum Nandrum is a sentiment polarity detection system using two approaches--a Stochastic Gradient Descent (SGD) based classifier and a Long Short-term Memory (LSTM) based Classifier. Our approach utilises language features like use of emoji, choice of scripts and code mixing which appeared quite marked in the datasets specified for the Dravidian Codemix - FIRE 2020 task. The hyperparameters for the SGD were tuned using GridSearchCV. Our system was ranked 4th in Tamil-English with a weighted average F1 score of 0.62 and 9th in Malayalam-English with a score of 0.65. We achieved a weighted a…
Insight SFI Research Centre for Data Analytics（Insight SFI数据分析研究中心）；National University of Ireland Galway（爱尔兰国立大学戈尔韦分校）；ULTRA Arts and Science College（ULTRA文理学院）；Indian Institute of Information Technology and Management-Kerala（印度信息技术与管理学院喀拉拉分校）；Sri Sivasubramaniya Nadar College of Engineering（斯里西瓦苏布拉马尼亚纳达尔工程学院）；Sri Krishna Adithya College of Arts and Science（斯里克里希纳阿迪蒂亚文理学院）；Hindusthan College of Arts and Science（印度斯坦文理学院）
The increased proliferation of abusive content on social media platforms has a negative impact on online users. The dread, dislike, discomfort, or mistrust of lesbian, gay, transgender or bisexual persons is defined as homophobia/transphobia. Homophobic/transphobic speech is a type of offensive language that may be summarized as hate speech directed toward LGBT+ people, and it has been a growing concern in recent years. Online homophobia/transphobia is a severe societal problem that can make online platforms poisonous and unwelcome to LGBT+ people while also attempting to eliminate equality, d…
Maulana Abul Kalam Azad University of Technology（毛拉纳·阿布尔·卡拉姆·阿扎德技术大学）；Carnegie Mellon University（卡内基梅隆大学）
On June 28, 2020, while presenting a chess podcast on Grandmaster Hikaru Nakamura, Antonio Radić's YouTube handle got blocked because it contained "harmful and dangerous" content. YouTube did not give further specific reason, and the channel got reinstated within 24 hours. However, Radić speculated that given the current political situation, a referral to "black against white", albeit in the context of chess, earned him this temporary ban. In this paper, via a substantial corpus of 681,995 comments, on 8,818 YouTube videos hosted by five highly popular chess-focused YouTube channels, we ask th…
A Context-Aware Dataset for Stance Detection in Bioethical Controversies on Reddit
School of Cyber Science and Technology, University of Science and Technology of China（中国科学技术大学网络空间安全学院）；School of Artificial Intelligence, Shenzhen Technology University（深圳技术大学人工智能学院）；School of Urban Planning and Design, Peking University（北京大学城市规划与设计学院）
Samarkand State University（撒马尔罕国立大学）；University of Hertfordshire（赫特福德大学）；Lund University（隆德大学）
Opinion mining on social media posts has become more and more popular. Users often express their opinion on a topic not only with words but they also use image symbols such as emoticons and emoji. In this paper, we investigate the effect of emoji-based features in opinion classification of Uzbek texts, and more specifically movie review comments from YouTube. Several classification algorithms are tested, and feature ranking is performed to evaluate the discriminative ability of the emoji-based features.
