Paper 1 : Predicting Depression via Social Media
Paper 2 : Understanding Anti-Vaccination Attitudes in Social Media
The two papers are more distinct than the previous papers for reflections, even though they deal with the issues within the same sphere.
Paper 1 takes a very important topic which is very relevant to everyone, predicting traits of depression through social posts.
The authors observe depression through tweets of the user. A user who is not much active/expressive on twitter cannot be of much help in predicting depression. On the other hand, active twitter users show a lot of sign of going into depression as well as on using anti-depressants. The authors observerd that the traits included negative langauge in tweets and lesser interactions with replies and DM. Are these results generizable to other social platforms ? Can a youtuber ‘s depression predicted by the facial experssions and language on his/her videos ? On extending it youtube, more parameters need to be taken into consideration.
Paper 2 analyses the behavior of anti-vaccinners. It observes how earlier concept of herd community to vaccination is failing due to online information sharing. The authors focus on three groups – pro-vaccine, anti-vaccine and those who recently switch to anti-vaccine and what triggers that. One interesting note in the paper is that they don’t take into account the people who switch to pro-vaccine and their triggers. I believe it will shed some light over what makes someone realise that they were part of a conspirationalist group. This can be used to create methods which reverse the effects of brainwashing by such anti-vaccine groups.
The paper uses MEM topic model to categories user tweets into themes of the tweets anti-vacciners tweet and care about. It doesn’t take into the account of virality of the news topic. For example, during the Syrian revolution, a lot of people were tweeting about government, war, violence etc. The authors don’t mention that if they have taken care to minimize effects of viral news of the tweet topics.
The anti-vacciners group show a close-knit group characteristics. This is equally true in real life also. People generally stay with other people with similar viewpoints or at least compatible view points. This brings to the paper’s conclusion which states that small triggers are enough for a person to join the anti-vaccine group. In my opinion, those people have a long exposure to such thinking outside of twitter and they get vocal only when they join a certain group.
