scholarly journals Understanding intent, behavior and emotion in user comments

2021 ◽  
Author(s):  
Galit Gordoni ◽  
Oren Soffer
Keyword(s):  
Social Behavior ◽  
Perceived Support ◽  
Spiral Of Silence ◽  
Negative Comment ◽  
User Comments ◽  
Personal Opinion ◽  
The Social ◽  
News Website ◽  
Negative Comments ◽  
The Spiral Of Silence

This study tests the process of writing a user comment on a news website: it follows the user's exposure to an online article, exposure to others’ comments, decision to write their own comment (or not), and the social behavior of writing itself, as well as the characteristics of the writing. We aim to examine whether exposure to positive and negative user comments on an online journalistic article affects the user's intention to send a comment and, more importantly, the sentiment of the comment posted. We also test whether a user's opinion or their perception of majority support of their opinion have an impact on either their intention to post a comment or their comments’ sentiment. Results show that exposure to comments, whether positive or negative, almost doubles the intentions of posting a comment. Exposure to negative comments dramatically increases the probability of writing a negative comment, while exposure to positive comments has a moderating effect. In line with the spiral of silence theory, self-perceived support for one’s opinion by the majority significantly contributes to the prediction of comment posting, even after controlling for the effect of personal opinion.

The Role of User Comments in Estimation of the Public Opinion Climate and Perceived Support for One’s Opinion

2019 ◽  
Vol 32 (3) ◽  
pp. 569-585
Author(s):  
Oren Soffer ◽  
Galit Gordoni
Keyword(s):  
Public Opinion ◽  
Perceived Support ◽  
Online Experiment ◽  
Public Discussion ◽  
The Public ◽  
User Comments ◽  
Study Results ◽  
Negative Comments

Abstract This article examines how user comments influence assessment of public opinion climate and perceived support for one’s opinion. The effects of user-comment sentiment (positive vs. negative) and of user-comment content (with or without personal exemplification) were tested with an online experiment (n = 1,510). Results show that user-comment effects on estimates of public opinion depend mainly on the sentiment of the comments and not on their framing as opinions with or without personal exemplification. Negative comments significantly reduce readers’ estimation of public opinion support of the issue dealt with by the article and affect the perceived support of one’s opinion. Study results refer to the possible dangers in user comments deliberate manipulation in democratic public discussion.

To comment or not to comment: Examining the influences of anonymity and social support on one’s willingness to express in online news discussions

2018 ◽  
Vol 20 (12) ◽  
pp. 4512-4532 ◽  
Author(s):  
Tai-Yee Wu ◽  
David J Atkin
Keyword(s):  
Social Support ◽  
Social Influence ◽  
Online News ◽  
The State ◽  
Perceived Support ◽  
Spiral Of Silence ◽  
Online Anonymity ◽  
The Spiral Of Silence ◽  
Different Sources

This study examines the effects of online anonymity and different sources of social influence on the Spiral of Silence phenomenon in online news discussions about abortion. The results ( N = 339) substantiated that technical anonymity predicts one’s perceived anonymity, but only the latter significantly increases one’s willingness to post personal opinions in the comment sections. Perceived support from other commenters was also found to reduce the online Spiral of Silence phenomenon. With fear of isolation, moreover, the state-based approach is verified to be more robust than the trait-like approach, advancing Noelle-Neumann’s original conceptualization. Study findings thus offer support for a more comprehensive conceptualization of Spiral of Silence components operating in online contexts.

The effects of likes on public opinion perception and personal opinion

2020 ◽  
Vol 45 (2) ◽  
pp. 223-239 ◽  
Author(s):  
Pablo Porten-Cheé ◽  
Christiane Eilders
Keyword(s):  
Information Processing ◽  
Public Opinion ◽  
Media Bias ◽  
Spiral Of Silence ◽  
Personal Opinion ◽  
The Media ◽  
The Spiral Of Silence ◽  
Heuristic Information Processing ◽  
Using Data ◽  
Public Opinion Perception

AbstractDrawing on the spiral of silence theory and heuristic information processing, we contend that individuals use likes as sources for assessing public opinion. We further argue that individuals may even adapt their personal opinions to the tenor reflected in those cues. The assumptions were tested using data from an experiment involving 501 participants, who encountered media items on two issues with or without likes. The findings show that respondents inferred public opinion from the media bias if it was supported by likes, however, only in cases of high levels of fear of social isolation. Respondents further adapted their personal opinion to the media bias if it was supported by likes.

Extending the Spiral of Silence: Partisan Media, Perceived Support, and Sharing Opinions Online

2017 ◽  
Vol 14 (3) ◽  
pp. 248-262 ◽  
Author(s):  
Meredith Y. Wang ◽  
Jay D. Hmielowski ◽  
Myiah J. Hutchens ◽  
Michael A. Beam
Keyword(s):  
Perceived Support ◽  
Spiral Of Silence ◽  
The Spiral Of Silence ◽  
Partisan Media

scholarly journals Sentiment Analysis of Food Recipe Comments

1970 ◽  
Vol 9 (2) ◽  
pp. 182-193
Author(s):  
Pakawan Pugsee ◽  
Monsinee Niyomvanich
Keyword(s):  
Sentiment Analysis ◽  
Text Analytics ◽  
Negative Comment ◽  
User Comments ◽  
Negative Comments ◽  
Positive Comment

Sentiment analysis of food recipe comments is to identify user comments about the food recipes to the positive or the negative comments. The proposed method is suitable for analysing comments or opinions about food recipes by counting the polarity words on the food domain. The benefit of this research is to help users to choose the preferred recipes from different food recipes on online food communities. To analyse food recipes, the comments of each recipe from members of the community will be collected and classified to neutral, positive or negative comments. All recipes’ comment messages are processed using text analytics and the generated polarity lexicon. Therefore, the user can gain the information to make a smart decision. The evaluation of the comment analysis shows that the accuracy of neutral and positive comment classification is about 90%. In addition, the accuracy of negative comment identification is more than 70%.

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