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Mitra, Tanushree, Scott Counts, and James W. Pennebaker. “Understanding Anti-Vaccination Attitudes in Social Media.”
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De Choudhury, Munmun, et al. “Predicting depression via social media.”
While both paper target serious and important issues, the first ones is more interesting of the two, perhaps due to the nature of the question. The fact that anti-vaxers are prone to believing in conspiracy theories and in general exhibit distrust and a phobia of sorts seems highly logical. I was surprised to see that while the paper highlighted people who joined the anti-vax group, it ignored the people who left! What are linguistic and topical queues that the transitions (anti-vax to pro-vax) exhibit. I believe that this is important to understand to be able to fight against the “self-sealing” quality of conspiracies or in this case anti-vaccination theories. Overall, though the paper was very convincing and thorough.
A follow up question would be to find the trends and growth patterns of these theories and how contageous they are and how long they take to die out. Another interesting thing that could mined is the source of the claims they make and the validity thereof, this would provide insight into the processes involved in the birth of these conspiracies.
Coming onto the second paper, i feel the main motive of the paper is to predict depression, however the choice of classifier or model as i always complain about is weak again. Since the features were hand designed interpretability wouldn’t have been a issue. Therefore, ensemble techniques should’ve been opted for. In particular gradient boosting should have been used here.
Regardless of the choice of the classifiers, a direct followup questions is whether the same techniques can be applied to new forms of social media e.g. Facebook posts aren’t limited to short sentences and dyads are mostly in personal messages, Instagram’s content heavily leans towards pictures and videos etc. Can image analysis provide better insights perhaps? i.e. do other forms of media e.g. pictures and videos that are not text contain a better signal? Another interesting followup question is that whether these episodes of depression are isolated case of it crowd mass depression?. A graph/network analysis might provide good insight
Coming from a more ethics perspective an important question is whether social media platforms have the right to even monitor depression or behavioral traits? If they do find a person who’s highly vulnerable what sort of action can they take? I’m interested to know what other people think of this.
Finally, i would like to mention that the most of the social media posts that i come across these day are either sarcastic or play-off on being super busy, stressed or sad in an attempt to be funny. I believe a lot of these posts would pollute the dataset and it doesn’t seem like the authors have catered for it. Simply relying on law of large numbers isn’t going to get rid of this issue because it’s more of a prevailing trend rather than an outlying one.
