Paper 1 by Mitra et al., focuses on understanding the Anti-Vaccination Attitude in social media. The authors have collected over 3 million tweets from Twitter, compared and contrasted their linguistic styles, topics of interest, social characteristics and underlying social cognitive dimensions. They have categorized users into 3 group: anti-vaccines, pro-vaccines, and joining-anti vaccine cohort. Their analysis majorly involves examining individual’s overt expressions towards vaccination in a social media platform. The data collection process involved 2 main phases wherein phase 1 involved extracting tweet sample from the Twitter Firehouse stream between January 1 and 5, 2012 and classified tweets based on 5 phrases. Using these phrases, they fetched more tweets spanning four calendar years. Post data collection the authors built a classifier to classify the collected posts as pro-vaccine and anti-vaccine tweets and using trigrams and hashtags as features they built a supervised learning classifier which gave an accuracy of 84.7%. The authors then segregated users into 3 groups: long-term advocates of pro and anti-vaccination attitude and new users adopting the anti-vaccination attitude. I really like the method adopted by Mitra et al, to analyze the “What” aspect of the topics which people generally talk about. The MEM topic modeling approach implemented by the authors looks quite convincing and I wonder as the authors suggest, how can this study be extended to other social media platforms? And will It produce similar results? I didn’t find anything unconvincing in the paper however, I wonder if the same approach can be applied to other domains apart from public health.
Paper 2 by De Choudhury et al, talks about the depression which is a serious challenge in personal and public health. The objective of this paper is to explore the potential use of social media to detect and diagnose the major depressive disorder in individuals. The authors have collected tweets of users who report being diagnosed with clinical depression using crowdsourcing. I wonder how can we differentiate if an individual’s posts depressing content on Twitter only to seek attention or they are actually depressed. The hypothesis: “changes in language, activity, and social ties may be used jointly to construct statistical models to detect and even predict MDD in a fine-grained manner”. Based on the individual’s social media behavior the authors have derived measures like user engagement and emotion, egocentric social graph, linguistic style, depressive language use, and mentions of antidepressant medications – to quantify an individual’s social media behavior. It was interesting that the authors conducted an auxiliary screening test in addition to the CES-D questionnaire to eliminate noisy responses. Although authors have not explicitly indicated in the HITs that the two tests were depression screening questionnaires, However, I believe that the questions in CES-D are quite obvious to make individuals understand that the questionnaire is related to depression. Hence, I am not quite sure if this approach would have helped minimize the possible bias. In the Prediction framework section of the paper where authors have described the models they have implemented to build the classifier, it would have been helpful if they would have given information of the dimensions after dimensionality reduction(PCA).
In the end, both the pair of papers presents some quite interesting results. Re-iterating what I have mentioned earlier, I didn’t find anything unconvincing in both the papers and was quite impressed by both the studies.
