Measuring the Diversity of Facebook Reactions to Research

• URL: http://arxiv.org/abs/2001.01029v1
• Date: Sat, 4 Jan 2020 03:41:44 GMT
• Title: Measuring the Diversity of Facebook Reactions to Research
• Authors: Cole Freeman, Hamed Alhoori, Murtuza Shahzad
• Abstract summary: We present a novel way of weighting click-based reactions that increases their comprehensibility. We use our metrics along with LDA topic models and statistical testing to investigate how users' emotional responses differ from one scientific topic to another. We find that there is generally a positive response to scientific research on Facebook, and that articles generating a positive emotional response are more likely to be widely shared.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online and in the real world, communities are bonded together by emotional consensus around core issues. Emotional responses to scientific findings often play a pivotal role in these core issues. When there is too much diversity of opinion on topics of science, emotions flare up and give rise to conflict. This conflict threatens positive outcomes for research. Emotions have the power to shape how people process new information. They can color the public's understanding of science, motivate policy positions, even change lives. And yet little work has been done to evaluate the public's emotional response to science using quantitative methods. In this paper, we use a dataset of responses to scholarly articles on Facebook to analyze the dynamics of emotional valence, intensity, and diversity. We present a novel way of weighting click-based reactions that increases their comprehensibility, and use these weighted reactions to develop new metrics of aggregate emotional responses. We use our metrics along with LDA topic models and statistical testing to investigate how users' emotional responses differ from one scientific topic to another. We find that research articles related to gender, genetics, or agricultural/environmental sciences elicit significantly different emotional responses from users than other research topics. We also find that there is generally a positive response to scientific research on Facebook, and that articles generating a positive emotional response are more likely to be widely shared---a conclusion that contradicts previous studies of other social media platforms.

Related papers

• Flood of Techniques and Drought of Theories: Emotion Mining in Disasters [5.392688959066589]
  Emotion mining has become a crucial tool for understanding human emotions during disasters. This paper aims to summarize existing research on emotion mining within disaster contexts. We aim to enhance the effectiveness and reliability of emotion mining methodologies.
  arXiv  Detail & Related papers  (2024-07-07T00:43:05Z)
• SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations [53.60993109543582]
  SemEval-2024 Task 3, named Multimodal Emotion Cause Analysis in Conversations, aims at extracting all pairs of emotions and their corresponding causes from conversations. Under different modality settings, it consists of two subtasks: Textual Emotion-Cause Pair Extraction in Conversations (TECPE) and Multimodal Emotion-Cause Pair Extraction in Conversations (MECPE) In this paper, we introduce the task, dataset and evaluation settings, summarize the systems of the top teams, and discuss the findings of the participants.
  arXiv  Detail & Related papers  (2024-05-19T09:59:00Z)
• The Role of Likes: How Online Feedback Impacts Users' Mental Health [1.0156836684627544]
  We analyse the impact of receiving online feedback on users' emotional experience, social connectedness and self-esteem. We find that experiencing little to no reaction from others does not only elicit negative emotions amongst users, but also induces low levels of self-esteem. In contrast, receiving much positive online feedback, evokes feelings of social connectedness and reduces overall loneliness.
  arXiv  Detail & Related papers  (2023-12-19T07:48:10Z)
• Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench [83.41621219298489]
  We evaluate Large Language Models' (LLMs) anthropomorphic capabilities using the emotion appraisal theory from psychology. We collect a dataset containing over 400 situations that have proven effective in eliciting the eight emotions central to our study. We conduct a human evaluation involving more than 1,200 subjects worldwide.
  arXiv  Detail & Related papers  (2023-08-07T15:18:30Z)
• Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts [61.723046082145416]
  We introduce CovidET (Emotions and their Triggers during Covid-19), a dataset of 1,900 English Reddit posts related to COVID-19. We develop strong baselines to jointly detect emotions and summarize emotion triggers. Our analyses show that CovidET presents new challenges in emotion-specific summarization, as well as multi-emotion detection in long social media posts.
  arXiv  Detail & Related papers  (2022-10-22T19:10:26Z)
• Affective Idiosyncratic Responses to Music [63.969810774018775]
  We develop methods to measure affective responses to music from over 403M listener comments on a Chinese social music platform. We test for musical, lyrical, contextual, demographic, and mental health effects that drive listener affective responses.
  arXiv  Detail & Related papers  (2022-10-17T19:57:46Z)
• Affection: Learning Affective Explanations for Real-World Visual Data [50.28825017427716]
  We introduce and share with the research community a large-scale dataset that contains emotional reactions and free-form textual explanations for 85,007 publicly available images. We show that there is significant common ground to capture potentially plausible emotional responses with a large support in the subject population. Our work paves the way for richer, more human-centric, and emotionally-aware image analysis systems.
  arXiv  Detail & Related papers  (2022-10-04T22:44:17Z)
• Public Reaction to Scientific Research via Twitter Sentiment Prediction [0.0]
  Social media users share their ideas, thoughts, and emotions with other users. It is not clear how online users would respond to new research outcomes. This study aims to predict the nature of the emotions expressed by Twitter users toward scientific publications.
  arXiv  Detail & Related papers  (2022-09-11T17:24:37Z)
• Validating daily social media macroscopes of emotions [0.12656629989060433]
  We run a large-scale survey at an online newspaper to gather daily self-reports of affective states from its users. We compare these with aggregated results of sentiment analysis of user discussions on the same online platform. For both platforms, we find strong correlations between text analysis results and levels of self-reported emotions.
  arXiv  Detail & Related papers  (2021-08-17T14:28:56Z)
• A Study on Herd Behavior Using Sentiment Analysis in Online Social Network [1.5673338088641469]
  This paper represents and analyze the capacity of diverse strategies to predict critical opinions from online social networking sites. Social media becomes a good outlet since the last decades to share the opinions globally. This study demonstrates the evaluation of sentiment analysis techniques using social media contents.
  arXiv  Detail & Related papers  (2021-07-25T05:22:35Z)
• MIME: MIMicking Emotions for Empathetic Response Generation [82.57304533143756]
  Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure. We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content.
  arXiv  Detail & Related papers  (2020-10-04T00:35:47Z)

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.