Facebook sentiment: Reactions and Emojis

Tian, Yuan; Galery, Thiago; Dulcinati, Giulio; Molimpakis, Emilia; Sun, Chao

doi:10.18653/v1/w17-1102

Cited by 94 publications

(80 citation statements)

References 11 publications

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“…Language barriers, typos, and spelling variations undetected during the analysis may have resulted in this difference. There was the potential that keywords may have been used for ironic or non-direct purposes, as seen with positive emojis [19]. This could not be detected via the methods of analysis used in this study.…”

Section: Discussionmentioning

confidence: 94%

“…Sad emojis (negative broad classification) generally do not have a major impact on the perception of an adjoining piece of text [18]. However, it must also be noted that positive emojis are commonly used with “ironic” and “polite” connotations as well as for direct positive meanings [19]. Furthermore, there was the additional potential that the attitude of public comments may not have reflected the study objective.…”

Section: Discussionmentioning

confidence: 99%

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Endangered Exotic Pets on Social Media in the Middle East: Presence and Impact

Spee

Hazel

Grande

et al. 2019

Animals

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The popularization of exotic pets on celebrity social media in the Middle East has led to questionable impacts on exotic pet demand and threats to species conservation. The objective of this study was to identify exotic animal species featured on Middle Eastern celebrity social media account posts, the public perception of those posts, and their potential impacts on exotic pet demand and conservation (for global-scale extrapolation). Public social media accounts of highly influential persons from oil-rich Middle Eastern regions were manually investigated to evaluate subject demographics, species features, and post information (likes, comments) between January 2017 and August 2018. Twenty-five subjects possessed active social media accounts, from which 418 social media posts were extracted based on their inclusion of a privately owned exotic animal. SPSS Version 25 was used for frequency and descriptive analyses of these posts, in addition to comment analyses to evaluate quantitative (emojis) and qualitative (text) audience perceptions from a total of 10 social media posts of CITES Appendix I- or II-listed species. A greater frequency of positive than negative comments was observed (n = 8017), demonstrating the higher likelihood of social media promotion rather than negation of the exotic pet trade. Public education on wildlife conservation and exotic animal trade risks is imperative for successful conservation and welfare protection.

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Section: Discussionmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Endangered Exotic Pets on Social Media in the Middle East: Presence and Impact

Spee

Hazel

Grande

et al. 2019

Animals

View full text Add to dashboard Cite

show abstract

“…Several studies have been conducted to measure and understand emotions using social media data [23][24][25][26][27]. Tian et al [28] studied the way Facebook users modify the sentiment of their comments with emojis. They targeted posts on public news pages, comparing three channels of expression: natural language, emojis, and reactions.…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring the Diversity of Facebook Reactions to Research

Freeman

Alhoori

Shahzad

2020

Proc. ACM Hum.-Comput. Interact.

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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.

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“…The sentiment part could refer to polarity [1], fine grained or not [2], or to pure emotion information [3][4][5]. The most common source of information for sentiment analysis is Online Social Networks (OSNs) [6,7]. User-generated content provides a unique combination of complexity and challenge for automated sentiment classification.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content

2020

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Sentiment analysis usually refers to the analysis of human-generated content via a polarity filter. Affective computing deals with the exact emotions conveyed through information. Emotional information most frequently cannot be accurately described by a single emotion class. Multilabel classifiers can categorize human-generated content in multiple emotional classes. Ensemble learning can improve the statistical, computational and representation aspects of such classifiers. We present a baseline stacked ensemble and propose a weighted ensemble. Our proposed weighted ensemble can use multiple classifiers to improve classification results without hyperparameter tuning or data overfitting. We evaluate our ensemble models with two datasets. The first dataset is from Semeval2018-Task 1 and contains almost 7000 Tweets, labeled with 11 sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments, labeled with six different levels of abuse or harassment. Our results suggest that ensemble learning improves classification results by 1.5 % to 5.4 % .

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Facebook sentiment: Reactions and Emojis

Cited by 94 publications

References 11 publications

Endangered Exotic Pets on Social Media in the Middle East: Presence and Impact

Endangered Exotic Pets on Social Media in the Middle East: Presence and Impact

Measuring the Diversity of Facebook Reactions to Research

Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content

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