Social media systems rely on user feedback and rating mechanisms for personalization, ranking, and content filtering. However, when users evaluate content contributed by fellow users (e.g., by liking a post or voting on a comment), these evaluations create complex social feedback effects. This paper investigates how ratings on a piece of content affect its author's future behavior. By studying four large comment-based news communities, we find that negative feedback leads to significant behavioral changes that are detrimental to the community. Not only do authors of negatively-evaluated content contribute more, but also their future posts are of lower quality, and are perceived by the community as such. Moreover, these authors are more likely to subsequently evaluate their fellow users negatively, percolating these effects through the community. In contrast, positive feedback does not carry similar effects, and neither encourages rewarded authors to write more, nor improves the quality of their posts. Interestingly, the authors that receive no feedback are most likely to leave a community. Furthermore, a structural analysis of the voter network reveals that evaluations polarize the community the most when positive and negative votes are equally split.
The setting considered in this paper, that of comments on news sites and blogs, has also been used to study other social phenomena such as controversy @cite_20 , political polarization @cite_22 @cite_8 , and community formation @cite_10 @cite_30 . News commenting systems have also been analyzed from a community design perspective @cite_2 @cite_16 @cite_32 , including a particular focus on understanding what types of articles are likely to attract a large volume of user comments @cite_28 @cite_15 . In contrast, our analysis focuses on the effects of voting on the behavior of the author whose content is being evaluated.
@cite_30 @cite_22 @cite_8 @cite_28 @cite_32 @cite_2 @cite_15 @cite_16 @cite_10 @cite_20
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Social media systems rely on user feedback and rating mechanisms for personalization, ranking, and content filtering. However, when users evaluate content contributed by fellow users (e.g., by liking a post or voting on a comment), these evaluations create complex social feedback effects. This paper investigates how ratings on a piece of content affect its author's future behavior. By studying four large comment-based news communities, we find that negative feedback leads to significant behavioral changes that are detrimental to the community. Not only do authors of negatively-evaluated content contribute more, but also their future posts are of lower quality, and are perceived by the community as such. Moreover, these authors are more likely to subsequently evaluate their fellow users negatively, percolating these effects through the community. In contrast, positive feedback does not carry similar effects, and neither encourages rewarded authors to write more, nor improves the quality of their posts. Interestingly, the authors that receive no feedback are most likely to leave a community. Furthermore, a structural analysis of the voter network reveals that evaluations polarize the community the most when positive and negative votes are equally split. Social media systems rely on user feedback and rating mechanisms for personalization, ranking, and content filtering. However, when users evaluate content contributed by fellow users (e.g., by liking a post or voting on a comment), these evaluations create complex social feedback effects. This paper investigates how ratings on a piece of content affect its author's future behavior. By studying four large comment-based news communities, we find that negative feedback leads to significant behavioral changes that are detrimental to the community. Not only do authors of negatively-evaluated content contribute more, but also their future posts are of lower quality, and are perceived by the community as such. Moreover, these authors are more likely to subsequently evaluate their fellow users negatively, percolating these effects through the community. In contrast, positive feedback does not carry similar effects, and neither encourages rewarded authors to write more, nor improves the quality of their posts. Interestingly, the authors that receive no feedback are most likely to leave a community. Furthermore, a structural analysis of the voter network reveals that evaluations polarize the community the most when positive and negative votes are equally split. Online user comments contain valuable user opinions. Comments vary greatly in quality and detecting high quality comments is a subtask of opinion mining and summarization research. Finding attentive comments that provide some reasoning is highly valuable in understanding the user’s opinion particularly in sociopolitical opinion mining and aids policy makers, social organizations or government sectors in decision making. In this paper we study the problem of detecting thoughtful comments. We empirically study various textual features, discourse relations and relevance features to predict thoughtful comments. We use logistic regression model and test on the datasets related to sociopolitical content. We found that the most useful features include the discourse relations and relevance features along with basic textual features to predict the comment quality in terms of thoughtfulness. In our experiments on two different datasets, we could achieve a prediction score of 79.37 and 73.47 in terms of F-measure on the two data sets, respectively. As crowd-sourced curation of news and information become the norm, it is important to understand not only how individuals consume information through social news Web sites, but also how they contribute to their ranking systems. In this paper, we introduce and make available a new data set containing the activity logs that recorded all activity for 309 Reddit users for one year. Using this newly collected data, we present findings that highlight the browsing and voting behavior of the study’s participants. We find that most users do not read the article that they vote on, and that, in total, 73 of posts were rated (i.e., upvoted or downvoted) without first viewing the content. We also show the evidence of cognitive fatigue in the browsing sessions of users that are most likely to vote.