bk<p>Key points :<br>1. To examine the influence of <a href="https://sfba.social/tags/news" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>news</span></a> source bias on <a href="https://sfba.social/tags/affective" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>affective</span></a> content and <a href="https://sfba.social/tags/virality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>virality</span></a> over time, we applied <a href="https://sfba.social/tags/sentiment" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sentiment</span></a> analysis to a large <a href="https://sfba.social/tags/socialmedia" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>socialmedia</span></a> dataset over a decade (i.e., ~ 30 M twitter posts from 2011-2020).<br>2. Biased news sources on both sides produced more arousing negative affective content, and this content was also most viral.<br>3. Biased news sources may generate " <a href="https://sfba.social/tags/AffectivePollution" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AffectivePollution</span></a> ”, engaging users at the expense of their knowledge, well-being, and harmony. (2/3)</p>
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#affectivepollution
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bk<p>Key points from "News source bias and sentiment on social media" : <br>1. To examine the influence of <a href="https://sfba.social/tags/news" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>news</span></a> source bias on <a href="https://sfba.social/tags/affective" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>affective</span></a> content and <a href="https://sfba.social/tags/virality" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>virality</span></a> over time, we applied <a href="https://sfba.social/tags/sentiment" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sentiment</span></a> analysis to a large social <a href="https://sfba.social/tags/media" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>media</span></a> dataset over a decade (i.e., ~ 30 million twitter posts from 2011-2020). <br>2. Biased news sources on both sides produced more high arousal negative affective content, and this content was also most viral.<br>3. The virality of high arousal negative content increased over a decade, particularly for balanced news sources. <br>4. Biased news sources may generate " <a href="https://sfba.social/tags/AffectivePollution" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>AffectivePollution</span></a> ” which engages users at the expense of their knowledge, well-being, and harmony. (2/3)</p>
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