In [1] The authors studied the attitudes of people who are against vaccines by analyzing the attitudes of participants involved in the vaccination debate on Twitter. They gathered 315240 tweets related to certain phrases from 144817 users in a 3 year time period. Then users were classified into pro vaccine group, anti vaccine group and joining anti vaccine group by comparing linguistic styles, topics of interest and social characteristics. The authors found that the long-term anti-vaccination supporters have conspiratorial views, mistrust in government and are resolute, and these supporters use more direct language and have higher expressions of anger compared to their pro counterparts.. Also, the “joining-anti” share similar conspiracy thinking but they tend to be less assured and more social in nature. I am curious when they first started the analysis, did they identify “typical twitter users” first and then did a manual analysis first? Because I would assume, for a persistent anti-vaccine person, his/her tweets will be consistently very aggressive. But in here, user’s tweets are not considered consistently. By using user ID that comes with tweet raw data, we might be able to find some conflicting users and filter out some noise data, or it would be interesting to see why there is such a conflict in attitude? Also, I would consider those users who constantly tweet about anti-vaccine to be extreme because people just don’t tweet about this. It would be interesting to see how anti-vaccine tweets spread in times of flu season, and how people view this issue. The spread pattern of tweets can show us who are those opinion leaders who can make an impact. To some sense, we can see this as potentially fake news detection, because those conspiracy stories can be defined as fake news.
[1] Mitra, Tanushree, Scott Counts, and James W. Pennebaker. “Understanding Anti-Vaccination Attitudes in Social Media.”
[2] De Choudhury, Munmun, et al. “Predicting depression via social media.”
