In this paper, we develop an LLM-powered framework for the curation and evaluation of emerging opinion mining in online health communities. We formulate emerging opinion mining as a pairwise stance detection problem between (title, comment) pairs sourced from Reddit, where post titles contain emerging health-related claims on a topic that is not predefined. The claims are either explicitly or implicitly expressed by the user. We detail (i) a method of claim identification -- the task of identifying if a post title contains a claim and (ii) an opinion mining-driven evaluation framework for stance detection using LLMs.
We facilitate our exploration by releasing a novel test dataset, Long COVID-Stance, or LC-stance, which can be used to evaluate LLMs on the tasks of claim identification and stance detection in online health communities. Long Covid is an emerging post-COVID disorder with uncertain and complex treatment guidelines, thus making it a suitable use case for our task. LC-Stance contains long COVID treatment related discourse sourced from a Reddit community. Our evaluation shows that GPT-4 significantly outperforms prior works on zero-shot stance detection. We then perform thorough LLM model diagnostics, identifying the role of claim type (i.e. implicit vs explicit claims) and comment length as sources of model error.
PERSPECTRA: A Scalable and Configurable Pluralist Benchmark of Perspectives from Arguments
Bonn-Aachen International Center for Information Technology, University of Bonn（波恩大学波恩-亚琛国际信息技术中心）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）；University of Sheffield（谢菲尔德大学）；McMaster University（麦克马斯特大学）
From Words to Wisdom: Discourse Annotation and Baseline Models for Student Dialogue Understanding
Department of Electrical and Computer Engineering, Tufts University（电气与计算机工程系，塔夫茨大学）；Department of Computer Science and Information Technology, Patuakhali Science and Technology University（计算机科学与信息技术系，帕图阿哈利科学与技术大学）；Department of Electrical and Computer Engineering, Computer Science and Biomedical Engineering, Tufts University（电气与计算机工程系，计算机科学与生物医学工程系，塔夫茨大学）；Department of Mechanical Engineering and Education, Tufts University（机械工程与教育系，塔夫茨大学）
Large Language Models' Varying Accuracy in Recognizing Risk-Promoting and Health-Supporting Sentiments in Public Health Discourse: The Cases of HPV Vaccination and Heated Tobacco Products
University of California Davis（加利福尼亚大学戴维斯分校）；Kyung Hee University（庆熙大学）；University of Massachusetts Boston（马萨诸塞大学波士顿分校）
Large Language Models Unpack Complex Political Opinions through Target-Stance Extraction
Department of Methodology and Statistics, Utrecht University（方法论与统计学系，乌特雷赫特大学）；Department of Languages, Literature and Communication, Utrecht University（语言、文学与传播系，乌特雷赫特大学）
Capturing Opinion Shifts in Deliberative Discourse through Frequency-based Quantum deep learning methods
Amity Center for Artificial Intelligence, Amity University（阿米蒂大学人工智能中心，阿米蒂大学）；Amity School of Engineering and Technology, Amity University（阿米蒂工程与技术学院，阿米蒂大学）
We analyze 26.2 million comments published in Arabic language on Twitter, from July 2014 to January 2015, when ISIS' strength reached its peak and the group was prominently expanding the territorial area under its control. By doing that, we are able to measure the share of support and aversion toward the Islamic State within the online Arab communities. We then investigate two specific topics. First, by exploiting the time-granularity of the tweets, we link the opinions with daily events to understand the main determinants of the changing trend in support toward ISIS. Second, by taking advanta…
The work presented in this paper attempts to evaluate and quantify the use of discourse relations in the context of blog summarization and compare their use to more traditional and factual texts. Specifically, we measured the usefulness of 6 discourse relations - namely comparison, contingency, illustration, attribution, topic-opinion, and attributive for the task of text summarization from blogs. We have evaluated the effect of each relation using the TAC 2008 opinion summarization dataset and compared them with the results with the DUC 2007 dataset. The results show that in both textual genr…
What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse
MaiNLP & MCML, LMU Munich, Germany（LMU慕尼黑媒体与传播系）；Department of Media and Communication, LMU Munich, Germany（LMU慕尼黑媒体与传播系）；National Research Council Canada, Ottawa, Canada（加拿大国家研究委员会）
Federal University of Rio Grande do Sul（南里奥格兰德联邦大学）；Federal University of Health Sciences of Porto Alegre（阿雷格里港联邦健康科学大学）
2018's Brazilian presidential elections highlighted the influence of alternative media and social networks, such as Twitter. In this work, we perform an analysis covering politically motivated discourses related to the second round in Brazilian elections. In order to verify whether similar discourses reinforce group engagement to personal beliefs, we collected a set of tweets related to political hashtags at that moment. To this end, we have used a combination of topic modeling approach with opinion mining techniques to analyze the motivated political discourses. Using SentiLex-PT, a Portugues…
The paper considers participles such as "unknown", "identified" and "unspecified", which in sentences such as "Solange is staying in an unknown hotel" have readings equivalent to an indirect question "Solange is staying in a hotel, and it is not known which hotel it is." We discuss phenomena including disambiguation of quantifier scope and a restriction on the set of determiners which allow the reading in question. Epistemic modifiers are analyzed in a DRT framework with file (information state) discourse referents. The proposed semantics uses a predication on files and discourse referents whi…
Amirkabir University of Technology（阿米尔卡比尔理工大学）；Allame Tabatabaei Universiy（阿拉梅·塔巴塔巴伊大学）；Tarbiat Modarres University（塔比亚特莫达雷斯大学）
One of the new scientific ways of understanding discourse dynamics is analyzing the public data of social networks. This research's aim is Post-structuralist Discourse Analysis (PDA) of Covid-19 phenomenon (inspired by Laclau and Mouffe's Discourse Theory) by using Intelligent Data Mining for Persian Society. The examined big data is five million tweets from 160,000 users of the Persian Twitter network to compare two discourses. Besides analyzing the tweet texts individually, a social network graph database has been created based on retweets relationships. We use the VoteRank algorithm to intr…
University College Dublin（都柏林大学学院）；University Bologna（博洛尼亚大学）
Doctors and nurses in these weeks are busy in the trenches, fighting against a new invisible enemy: Covid-19. Cities are locked down and civilians are besieged in their own homes, to prevent the spreading of the virus. War-related terminology is commonly used to frame the discourse around epidemics and diseases. Arguably the discourse around the current epidemic will make use of war-related metaphors too,not only in public discourse and the media, but also in the tweets written by non-experts of mass communication. We hereby present an analysis of the discourse around #Covid-19, based on a cor…
University of Liverpool（利物浦大学）；University of Cambridge（剑桥大学）；The Alan Turing Institute（艾伦·图灵研究所）
Social media's growing popularity raises concerns around children's online safety. Interactions between minors and adults with predatory intentions is a particularly grave concern. Research into online sexual grooming has often relied on domain experts to manually annotate conversations, limiting both scale and scope. In this work, we test how well-automated methods can detect conversational behaviors and replace an expert human annotator. Informed by psychological theories of online grooming, we label $6772$ chat messages sent by child-sex offenders with one of eleven predatory behaviors. We …
Aspect-level sentiment analysis aims to determine the sentiment polarity towards a specific target in a sentence. The main challenge of this task is to effectively model the relation between targets and sentiments so as to filter out noisy opinion words from irrelevant targets. Most recent efforts capture relations through target-sentiment pairs or opinion spans from a word-level or phrase-level perspective. Based on the observation that targets and sentiments essentially establish relations following the grammatical hierarchy of phrase-clause-sentence structure, it is hopeful to exploit compr…
Identifying and communicating relationships between causes and effects is important for understanding our world, but is affected by language structure, cognitive and emotional biases, and the properties of the communication medium. Despite the increasing importance of social media, much remains unknown about causal statements made online. To study real-world causal attribution, we extract a large-scale corpus of causal statements made on the Twitter social network platform as well as a comparable random control corpus. We compare causal and control statements using statistical language and sen…
Citing opinions is a powerful yet understudied strategy in argumentation. For example, an environmental activist might say, "Leading scientists agree that global warming is a serious concern," framing a clause which affirms their own stance ("that global warming is serious") as an opinion endorsed ("[scientists] agree") by a reputable source ("leading"). In contrast, a global warming denier might frame the same clause as the opinion of an untrustworthy source with a predicate connoting doubt: "Mistaken scientists claim [...]." Our work studies opinion-framing in the global warming (GW) debate,…
In sentiment analysis, the polarities of the opinions expressed on an object/feature are determined to assess the sentiment of a sentence or document whether it is positive/negative/neutral. Naturally, the object/feature is a noun representation which refers to a product or a component of a product, let us say, the "lens" in a camera and opinions emanating on it are captured in adjectives, verbs, adverbs and noun words themselves. Apart from such words, other meta-information and diverse effective features are also going to play an important role in influencing the sentiment polarity and contr…
Since the onset of the COVID-19 pandemic, vaccines have been an important topic in public discourse. The discussions around vaccines are polarized as some see them as an important measure to end the pandemic, and others are hesitant or find them harmful. This study investigates posts related to COVID-19 vaccines on Twitter and focuses on those which have a negative stance toward vaccines. A dataset of 16,713,238 English tweets related to COVID-19 vaccines was collected covering the period from March 1, 2020, to July 31, 2021. We used the Scikit-learn Python library to apply a support vector ma…
Earthquakes have a deep impact on wide areas, and emergency rescue operations may benefit from social media information about the scope and extent of the disaster. Therefore, this work presents a text miningbased approach to collect and analyze social media data for early earthquake impact analysis. First, disasterrelated microblogs are collected from the Sina microblog based on crawler technology. Then, after data cleaning a series of analyses are conducted including (1) the hot words analysis, (2) the trend of the number of microblogs, (3) the trend of public opinion sentiment, and (4) a key…
Online debate forums provide users a platform to express their opinions on controversial topics while being exposed to opinions from diverse set of viewpoints. Existing work in Natural Language Processing (NLP) has shown that linguistic features extracted from the debate text and features encoding the characteristics of the audience are both critical in persuasion studies. In this paper, we aim to further investigate the role of discourse structure of the arguments from online debates in their persuasiveness. In particular, we use the factor graph model to obtain features for the argument stru…
Victoria University（维多利亚大学）；The University of Melbourne（墨尔本大学）
Domestic Violence (DV) is considered as big social issue and there exists a strong relationship between DV and health impacts of the public. Existing research studies have focused on social media to track and analyse real world events like emerging trends, natural disasters, user sentiment analysis, political opinions, and health care. However there is less attention given on social welfare issues like DV and its impact on public health. Recently, the victims of DV turned to social media platforms to express their feelings in the form of posts and seek the social and emotional support, for sym…
University of Sheffield（谢菲尔德大学）；Emotech（Emotech（爱墨科技））
Online political advertising is a central aspect of modern election campaigning for influencing public opinion. Computational analysis of political ads is of utmost importance in political science to understand the characteristics of digital campaigning. It is also important in computational linguistics to study features of political discourse and communication on a large scale. In this work, we present the first computational study on online political ads with the aim to (1) infer the political ideology of an ad sponsor; and (2) identify whether the sponsor is an official political party or a…
An increase in the use of smartphones has laid to the use of the internet and social media platforms. The most commonly used social media platforms are Twitter, Facebook, WhatsApp and Instagram. People are sharing their personal experiences, reviews, feedbacks on the web. The information which is available on the web is unstructured and enormous. Hence, there is a huge scope of research on understanding the sentiment of the data available on the web. Sentiment Analysis (SA) can be carried out on the reviews, feedbacks, discussions available on the web. There has been extensive research carried…
Gendered Divides in Online Discussions about Reproductive Rights
Thomas Lord Department of Computer Science, University of Southern California（美国南加州大学计算机科学系）；Information Sciences Institute, University of Southern California（美国南加州大学信息科学研究所）；Department of Psychology, Cornell University（康奈尔大学心理学系）；Luddy School of Informatics, Indiana University（印第安纳大学琳迪信息学院）
