Summary
The authors examine the attitudes of people who are against vaccines. They compare them with a pro-vaccine group and to their differences with the people are just joining the anti-vaccination camp. The data is four years of longitudinal data from Twitter, capturing vaccination discussions on Twitter. They identify three groups: those who are persistently pro vaccine, those who are persistently anti vaccine and users who newly join the anti-vaccination cohort. After fetching each cohort’s entire timeline of tweets, totaling to more than 3 million tweets, we compare and contrast their linguistic styles, topics of interest, social characteristics and underlying cognitive dimensions. Subsequently, they built a classifier to determine positive and negative attitudes towards vaccination. They find that people holding persistent anti-vaccination attitudes use more direct language and have higher expressions of anger compared to their pro counterparts. Adopters of anti-vaccine attitudes show similar conspiratorial ideation and suspicion towards the government.
Reflections
The article stresses that alternative methods should be adopted (non-official sources) in order to change the opinion of those who belong in the anti-vaccination group. However, this would work on the targeted groups who have anti-vaccination attitudes. If the informational method changes, it might have adverse effects, in the sense that it might revert pro-vaccination people into anti-vaccination.
I wonder if they could use unsupervised learning and perform and explorative analysis in order to find more groups of people. In addition, I didn’t know that population attitudes extracted from tweet sentiments has been shown to correlate with traditional polling data.
For the first phase, the authors use snowball samples. However, such samples are subject to numerous biases. For instance, people who have many friends are more likely to be recruited into the sample. I also find it interesting that the final set of words basically included a permutation of the words: mmr, autism, vaccine and measles. Is this what anti vaccination groups mainly focus on? The authors use a qualitative examination and find that trigrams and hashtags were prominent cues of a tweet’s stance towards vaccination. Interestingly enough, only “Organic Food” is statistically significant both Between Groups and Within Time.
Questions
- What kind of qualitative examination made the authors choose trigrams and hastaghs as the prominent cues of a tweet’s stance towards vaccination?
- I wonder whether the authors could find more than the three groups by using an unsupervised learning method.
- The number of Pre-Time Tweets are significantly less than Post-Time Tweets. Was that intentional?
Second Paper Reviewed
[2] Choudhury, M.D., Counts, S., Gamon, M., & Horvitz, E. (2013). Predicting Depression via Social Media. ICWSM.
Summary
The mail goal of the paper is to predict Major Depressive Disorder (henceforth MDD), as the title suggests, through social media. They author collect their data via crowdsourcing, specifically Amazon Turk. They ask them to complete a standardized depression form (CES-D) and they compare the answers to another standardized form (BID) in order to see whether they are correlated. They quantify the user’s behavior through their Twitter posts. They include two groups those who do suffer from depression and those who do not and they compare the two groups. Finally, they build a classifier that predicts MMD which has an accuracy of 70%.
The authors suggest that Twitter posts contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement.
Reflections
It should be noted that the classification is based on the behavioral attributes of people who already had a depression. Did they ask them for how long they suffer from a major depression disorder? I imagine someone who has been diagnosed for having depression years ago might have different behavioral attributes compared to someone who has been diagnosed a few months ago. In addition, being diagnosed as having depression is not equivalent to the actual onset of depression.
What if they collected the pre-depression onset tweets and compare them with the post-depression tweets? That might be an interesting extension. In addition, since the tweets are from the same individuals, factors that do not change temporarily could be controlled.
Something that puzzles me is their seemingly ad-hoc choice of onset dates. Specifically, they keep individuals with depression onset dates anytime in the last one year, but no later than three months prior to the day the survey was taken. Are they discarding individuals who have depression onset dates for more than one year? There is an implicit assumption that people who suffer from MMD are homogeneous.
Questions
- Why do they keep depression onset dates within the last year? Why not go further back?
- There is an implicit assumption by the authors. That people who suffer from a MDD are the same (i.e. homogeneous). Is someone who suffers from MDD for years the same as someone suffers for a few months? This lack of distinction might affect the classification model.
- An extension would be to study the twitter posts of the people who have MDD through time. Specifically, pre-MDD vs post-MDD behavior, for the same users. Since they are the same users, they will be able to control for factors that do not change through time.
