De Choudhury, Munmun, Michael Gamon, Scott Counts, and Eric Horvitz. “Predicting depression via social media.” ICWSM13 (2013): 1-10.
Summary & Reflection:
Tens of millions of people around the world each year suffer from depression but global provisions and services for identifying, supporting, and treating mental illness of this nature have been considered as insufficient. Since people’s virtual activities on social media can potentially indicate their mental state to some degree, the paper explores the potential to use social media to detect and diagnose the major depressive disorder in individuals. By compiling a set of Twitter users who report being diagnosed with clinical depression and observing their social media postings over a year preceding the onset of depression, they measure and feed behavioral attributes, such as social engagement, emotion, language and linguistic styles, to a statistical classifier that estimates of the risk of depression. Results indicate there are useful signals for characterizing the onset of depression, which can further instrumental in developing practical detection tools for depression.
I’m pretty impressed by using the Amazon’s Mechanical Turk interface to conduct clinical depression survey, which obviously a great advance that can cover more participates. But for the survey design, there is still one common question that whether the participants can remain objective and honest when answering the questionnaire and providing self-reported information since respondents would Because sometimes the respondent would unconsciously or even consciously hide their true situation. Although I was thinking about how to improve it, I did not think of a better way. But What we need to pay special attention to is that the design of the questions, which should appropriately guide the psychological state of the participants. The problem should not be blunt or irritating. For Measuring Depressive Behavior, I’m impressed by some of the measures such as defining the egocentric social graph, but I’m not convinced by some hypothesis like “Individuals with depression condition are likely to use these names in their posts”. In my intuition, I do not think depression patients will positively desire to receive feedback on their effects during the course of treatment. I also strongly feel that one of the most important things in social science research is actually to alleviate biases existing in many places, e.g., the authors in this paper conduct an auxiliary screening test in addition to the CES-D questionnaire to get rid of noisy responses.
Mitra, Tanushree, Scott Counts, and James W. Pennebaker. “Understanding Anti-Vaccination Attitudes in Social Media.” In ICWSM, pp. 269-278. 2016.
Summary & Reflection:
Public health can be threatened by an anti-vaccination movement which reduces the likelihood of disease eradication. Anti-vaccine information can be disseminated on social media like Twitter, thus Twitter data would help understand the drivers of attitudes among participants involved in the vaccination debate. By collecting tweets of users who persistently hold pro and anti-attitudes, and those who newly adopt anti attitudes towards vaccination, they find that those with long-term anti-vaccination attitudes manifest conspiratorial thinking, mistrust in government, and are resolute and in-group focused in language.
By comparing linguistic styles, topics of interest and social characteristics of over 3 million tweets from Twitter, Mitra et al categorize users into 3 group: anti-vaccines, pro-vaccines, and joining-anti vaccine cohort. The data collection process involves 2 main phases where they extracted tweet sample from the Twitter Firehouse stream in the first phase and built a classifier to classify the collected posts as pro-vaccine and anti-vaccine tweets. The MEM model that extracts dimensions along which users express themselves seems pretty interesting and it should be a good tool in other potential areas such as personalized recommendation functionality of the social platform since it can capture clusters of co-occurring words which can identify linguistic dimensions that represent psychologically meaningful themes.
