An Online Risk Index for the Cross-Sectional Prediction of New HIV Chlamydia, and Gonorrhea Diagnoses Across U.S. Counties and Across Years

Abstract

The present study evaluated the potential use of Twitter data for providing risk indices of STIs. We developed online risk indices (ORIs) based on tweets to predict new HIV, gonorrhea, and chlamydia diagnoses, across U.S. counties and across 5 years. We analyzed over one hundred million tweets from 2009 to 2013 using open-vocabulary techniques and estimated the ORIs for a particular year by entering tweets from the same year into multiple semantic models (one for each year). The ORIs were moderately to strongly associated with the actual rates (.35 < rs < .68 for 93% of models), both nationwide and when applied to single states (California, Florida, and New York). Later models were slightly better than older ones at predicting gonorrhea and chlamydia, but not at predicting HIV. The proposed technique using free social media data provides signals of community health at a high temporal and spatial resolution.

Prospective associations of regional social media messages with attitudes and actual vaccination: A big data and survey study of the influenza vaccine in the United States

“…In total, we collected 628,597 tweets and geotagged about 18% of the collected tweets for the analyses (see Table S1). This percentage is similar to the geotagging rates reported in previous studies [48] , [49] .…”

“…Tweets, including retweets, are informative about popular topics and conversations within a community. Prior studies have shown that county-level tweets can provide signals for predicting HIV/STI infections [49] , [54] and are associated with attitudes, knowledge, and behaviors of infectious diseases such as Zika across U.S. counties [55] . Therefore, we decided to use county as the unit of analysis.…”

“…We used the timestamp metadata to exclude tweets that do not originate from U.S. time zones. For each tweet, the user profile location and each tweet’s precise latitude and longitude coordinates were used to map tweets to U.S. counties using Geographic Information Systems (GIS) database operations (see [49] for details of the geotagging process). Tweets from the same county were organized into a single document, which constituted the unit of analysis.…”

Building a social media-based HIV risk behavior index to inform the prediction of HIV new diagnosis: a feasibility study

“…sex, drug use, HIV risk) were positively associated with HIV prevalence. Expanding this direction of research, Chan et al [7] created an Online Risk Index (ORI) based on tweets as a signal or proxy of county-level HIV, gonorrhea, and chlamydia rates. In addition to Twitter, Google Trends (google search engine) has also been used as a data source for HIV surveillance.…”

Estimating the influence of Twitter on pre-exposure prophylaxis use and HIV testing as a function of rates of men who have sex with men in the United States

“…Yet, other topics refer to love and celebrations, like 'romance' and 'raw' (topic 66), or 'birthday' and 'celebrating' (topic 45). More generally, topics refer to issues but have no literal interpretation (for a discussion of this issue) [11].…”