Does exposure to like-minded/non-like-minded information lead to the use of political incivility? Few studies have investigated this question, and the results have been mixed. There are two conflicting possibilities: (i) if individuals are frequently exposed to like-minded political information, they reinforce their pre-existing beliefs and are, thus, more likely to use uncivil language, and (ii) if individuals are frequently exposed to non-like-minded information, they often feel negative emotions and are, therefore, more likely to use incivility. To evaluate these two competing hypotheses, I analyze Japanese Twitter data using a semi-supervised learning method. The results show that individuals who are exposed to non-like-minded information are more likely to use political incivility.
Does exposure to like- and non-like-minded information lead to political incivility? Few previous studies have investigated this question, and the results have been mixed. There are two conflicting possibilities: (i) if individuals are frequently exposed to like-minded political information, their preexisting beliefs are reinforced and they are more likely to use uncivil language, and (ii) if individuals are frequently exposed to non-like-minded information, they often feel negative emotions and therefore are more likely to be uncivil. To evaluate these two competing hypotheses, the present study analyzes data from Japanese Twitter using a semi-supervised machine learning method. The results show that individuals who are exposed to non-like-minded information are more prone to political incivility.
Does exposure to like-minded/non-like-minded information lead to the use of political incivility? Few studies have investigated this question, and the results have been mixed. There are two conflicting possibilities: (i) if individuals are frequently exposed to like-minded political information, they reinforce their pre-existing beliefs and are, thus, more likely to use uncivil language, and (ii) if individuals are frequently exposed to non-like-minded information, they often feel negative emotions and are, therefore, more likely to use incivility. To evaluate these two competing hypotheses, I analyze Japanese Twitter data using a semi-supervised learning method. The results show that individuals who are exposed to non-like-minded information are more likely to use political incivility.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.