We investigate perceptions of tweets marked with the #BlackLivesMatter and #AllLivesMatter hashtags, as well as how the presence or absence of those hashtags changed the meaning and subsequent interpretation of tweets in U.S. participants. We found a strong effect of partisanship on perceptions of the tweets, such that participants on the political left were more likely to view #AllLivesMatter tweets as racist and offensive, while participants on the political right were more likely to view #BlackLivesMatter tweets as racist and offensive. Moreover, we found that political identity explained evaluation results far better than other measured demographics. Additionally, to assess the influence of hashtags themselves, we removed them from tweets in which they originally appeared and added them to selected neutral tweets. 

Currently, the world is experiencing a global pandemic concerning SARS-CoV-2. Scientific and medical information concerning the virus is being discovered and relayed quickly to try and, among other things, best inform the general public, and policy makers about how best to respond. This creates an opportunity to study the spread of scientific information and misinformation on social media platforms, which serves as the purpose of this research. Primary research objectives for this research are to examine how individual difference variables predict information sharing behaviors.

We extend this project by studying retweet network of viral misinformation tweets about COVID-19 regarding conspiracies or potential treatments and cures.

Social media has recently served as a platform to discuss political topics, and naturally, debate arises. Often times, viral hashtags emerge as a means to call attention to acts of injustice. In response, those who disagree with the sentiment of such viral tweets often create counter hashtags to express disagreement and call attention to their respective stances. Insight into the conversations that happen during these disagreements can be informative about the way in which individuals disagree on issues online. We propose to analyze, compare, and contrast opposing hashtags in two ways: (1) with measures of the Jensen-Shannon Divergence and Shannon entropy and (2) with distance as measured by (BERT) word embeddings.

The volume of tweets containing each respective hashtag naturally fluctuates over time, often peaking when the death of an individual is highly publicized. As such, there is lower engagement with each hashtag at times between viral tragedies. Potential exists for #BlackLivesMatter and #AllLivesMatter tweets to be related in more nuanced ways, both characteristically and, more notably, mathematically  (i.e. does the increase in the use of one hashtag contribute to the increase or decrease of the other? Or vice versa?).