A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets

• URL: http://arxiv.org/abs/2211.03533v1
• Date: Mon, 7 Nov 2022 13:19:44 GMT
• Title: A Multi-task Model for Sentiment Aided Stance Detection of Climate Change Tweets
• Authors: Apoorva Upadhyaya, Marco Fisichella, Wolfgang Nejdl
• Abstract summary: We propose a framework that helps identify denier statements on Twitter and thus classifies the stance of the tweet into one of the two attitudes towards climate change (denier/believer) We propose a multi-task framework that performs stance detection (primary task) and sentiment analysis (auxiliary task) simultaneously.
• Score: 2.111703012534138
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Climate change has become one of the biggest challenges of our time. Social media platforms such as Twitter play an important role in raising public awareness and spreading knowledge about the dangers of the current climate crisis. With the increasing number of campaigns and communication about climate change through social media, the information could create more awareness and reach the general public and policy makers. However, these Twitter communications lead to polarization of beliefs, opinion-dominated ideologies, and often a split into two communities of climate change deniers and believers. In this paper, we propose a framework that helps identify denier statements on Twitter and thus classifies the stance of the tweet into one of the two attitudes towards climate change (denier/believer). The sentimental aspects of Twitter data on climate change are deeply rooted in general public attitudes toward climate change. Therefore, our work focuses on learning two closely related tasks: Stance Detection and Sentiment Analysis of climate change tweets. We propose a multi-task framework that performs stance detection (primary task) and sentiment analysis (auxiliary task) simultaneously. The proposed model incorporates the feature-specific and shared-specific attention frameworks to fuse multiple features and learn the generalized features for both tasks. The experimental results show that the proposed framework increases the performance of the primary task, i.e., stance detection by benefiting from the auxiliary task, i.e., sentiment analysis compared to its uni-modal and single-task variants.

Related papers

• Show Me What and Where has Changed? Question Answering and Grounding for Remote Sensing Change Detection [82.65760006883248]
  We introduce a new task named Change Detection Question Answering and Grounding (CDQAG) CDQAG extends the traditional change detection task by providing interpretable textual answers and intuitive visual evidence. We construct the first CDQAG benchmark dataset, termed QAG-360K, comprising over 360K triplets of questions, textual answers, and corresponding high-quality visual masks.
  arXiv  Detail & Related papers  (2024-10-31T11:20:13Z)
• Understanding Opinions Towards Climate Change on Social Media [2.31449645503075]
  We aim to understand how real world events influence the opinions of individuals towards climate change related topics on social media. We extracted and analyzed a dataset of 13.6 millions tweets sent by 3.6 million users from 2006 to 2019. Our work acts as a first step towards understanding the evolution of pro-climate change communities around COP events.
  arXiv  Detail & Related papers  (2023-12-02T20:02:34Z)
• ClimateNLP: Analyzing Public Sentiment Towards Climate Change Using Natural Language Processing [0.0]
  This paper employs natural language processing (NLP) techniques to analyze climate change discourse and quantify the sentiment of climate change-related tweets. The objective is to discern the sentiment individuals express and uncover patterns in public opinion concerning climate change.
  arXiv  Detail & Related papers  (2023-10-12T07:48:50Z)
• Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging [29.413444722550356]
  We analyze how industries, their advocacy group, and climate advocacy group use social media to influence the narrative on climate change. We propose a minimally supervised model soup [57] approach combined with messaging themes to identify the stances of climate ads on Facebook.
  arXiv  Detail & Related papers  (2023-05-06T16:43:29Z)
• EgoTaskQA: Understanding Human Tasks in Egocentric Videos [89.9573084127155]
  EgoTaskQA benchmark provides home for crucial dimensions of task understanding through question-answering on real-world egocentric videos. We meticulously design questions that target the understanding of (1) action dependencies and effects, (2) intents and goals, and (3) agents' beliefs about others. We evaluate state-of-the-art video reasoning models on our benchmark and show their significant gaps between humans in understanding complex goal-oriented egocentric videos.
  arXiv  Detail & Related papers  (2022-10-08T05:49:05Z)
• Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet [98.97255654573662]
  This work provides an overview of areas in which audio intelligence can contribute to overcome climate-related challenges. We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether.
  arXiv  Detail & Related papers  (2022-03-10T13:32:31Z)
• Trend and Thoughts: Understanding Climate Change Concern using Machine Learning and Social Media Data [3.7384509727711923]
  We constructed a massive climate change Twitter dataset and conducted comprehensive analysis using machine learning. By conducting topic modeling and natural language processing, we show the relationship between the number of tweets about climate change and major climate events. Our dataset was published on Kaggle and can be used in further research.
  arXiv  Detail & Related papers  (2021-11-06T19:59:03Z)
• Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents [83.52684405389445]
  We introduce the collaborative multi-object navigation task CoMON. In this task, an oracle agent has detailed environment information in the form of a map. It communicates with a navigator agent that perceives the environment visually and is tasked to find a sequence of goals. We show that the emergent communication can be grounded to the agent observations and the spatial structure of the 3D environment.
  arXiv  Detail & Related papers  (2021-10-12T06:56:11Z)
• News consumption and social media regulations policy [70.31753171707005]
  We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation. Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content. The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
  arXiv  Detail & Related papers  (2021-06-07T19:26:32Z)
• ClimaText: A Dataset for Climate Change Topic Detection [2.9767565026354186]
  We introduce textscClimaText, a dataset for sentence-based climate change topic detection. We find that popular keyword-based models are not adequate for such a complex and evolving task. Our analysis reveals a great potential for improvement in several directions.
  arXiv  Detail & Related papers  (2020-12-01T13:42:37Z)
• Information Consumption and Social Response in a Segregated Environment: the Case of Gab [74.5095691235917]
  This work provides a characterization of the interaction patterns within Gab around the COVID-19 topic. We find that there are no strong statistical differences in the social response to questionable and reliable content. Our results provide insights toward the understanding of coordinated inauthentic behavior and on the early-warning of information operation.
  arXiv  Detail & Related papers  (2020-06-03T11:34:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.