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Background: Studies examining how sentiment on social media varies dependingon timing and location appear to produce inconsistent results, making it hardto design systems that use sentiment to detect localized events for publichealth applications.  Objective: The aim of this study was to measure how common timing andlocation confounders explain variation in sentiment on Twitter.  Methods: Using a dataset of 16.54 million English-language tweets from 100cities posted between July 13 and November 30, 2017, we estimated the positiveand negative sentiment for each of the cities using a dictionary-basedsentiment analysis and constructed models to explain the differences insentiment using time of day, day of week, weather, city, and interaction type(conversations or broadcasting) as factors and found that all factors wereindependently associated with sentiment.  Results: In the full multivariable model of positive (Pearson r in test data0.236; 95\% CI 0.231-0.241) and negative (Pearson r in test data 0.306; 95\% CI0.301-0.310) sentiment, the city and time of day explained more of the variancethan weather and day of week. Models that account for these confounders producea different distribution and ranking of important events compared with modelsthat do not account for these confounders.  Conclusions: In public health applications that aim to detect localizedevents by aggregating sentiment across populations of Twitter users, it isworthwhile accounting for baseline differences before looking for unexpectedchanges.

jmir. journal of medical internet research/journal of medical internet research

Modeling Spatiotemporal Factors Associated With Sentiment on Twitter:  Synthesis and Suggestions for Improving the Identification of Localized  Deviations