JMIR Publications

ABSTRACT

Background:

Reddit is a popular social media platform that has faced scrutiny for inflammatory language against those with obesity, yet there has been no comprehensive analysis of its obesity-related content.

Objective:

To quantify the presence of four types of obesity-related content on Reddit (misinformation, stigma, positivity, and facts) and identify psycholinguistic features that may be enriched within each one.

Methods:

All sentences (n = 764,179) containing “obese” or “obesity” from top-level comments (n = 689,447) made on non-age-restricted subreddits (i.e., smaller communities within Reddit) between 2011 and 2019 that contained one of a series of keywords were evaluated. Four types of common natural language processing features were extracted to train a machine learning classifier to label each sentence as one of the four content categories or ambiguous/other.

Results:

After removing ambiguous sentences, 3,610 sentences were labeled as misinformation, 14,366 sentences were labeled as stigma, 14,799 were labeled as positivity, and 68,276 were labeled as facts. Each category had markers that distinguished it from other categories within the data as well as an external corpus. For example, misinformation had a higher average percent of negations (β = 3.71, 95% CI: 3.53, 3.90, P<.001) but lower average number of words greater than six letters (β = -1.47, 95% CI: -1.85, -1.10, P<.001) relative to facts. Stigma had a higher proportion of swear words (β=1.83, 95% CI: 1.62, 2.04, P<.001) but lower proportion of first-person singular pronouns (β = -5.30, 95% CI: -5.44, -5.16, P<.001) relative to positivity.

Conclusions:

There are distinct psycholinguistic properties between types of obesity-related content on Reddit that can be leveraged to rapidly identify deleterious content with minimal human intervention. Future work should assess whether these properties are shared across languages and other social media.