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Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study

May 25, 2021

Journal of Medical Internet Research This paper is in the following e-collection/theme issue: January 11, 2021 . Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study Authors of this article: 2Computer Science, University of New Mexico, Albuquerque, NM, United States Corresponding Author: Analytics, Intelligence, and Technology Abstract Background: Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak. Objective: We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications. Methods: We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here. Results: We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to –0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States. Conclusions: Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors. J Med Internet Res 2021;23(5):e27059 Principal Findings The ongoing COVID-19 outbreak clearly illustrates the need for real-time information gathering to assess evolving beliefs and behaviors that directly impact disease spread. Historically, such information would be gathered using survey methods [ 5 , 7 , 31 ], which are time-consuming, expensive, and typically lack the ability to measure temporal and spatial variation [ 32 ]. One proposed partial solution is to use internet data (eg, search query patterns and social media data), which have been shown to correspond to disease incidence in emergent infectious disease outbreaks [ 23 , 33 - 35 ], individual risk perception [ 1 , 36 , 37 ], and risk communication [ 38 ], and have been used to identify specific health behaviors [ 15 ]. During the early stages of the current COVID-19 pandemic, social media data have been used to monitor the top concerns of individuals [ 39 , 40 ], characterize COVID-19 awareness [ 41 ], compare social connectedness and COVID-19 hot spots [ 42 ], monitor misinformation [ 40 , 43 - 45 ], and rapidly disseminate information [ 46 ]. Last, social media has been used as an information gathering platform during periods of uncertain information. Disease emergence is a context wherein disease risks, transmission, and treatment may be largely unclear [ 46 ]. With this context in mind, we address our findings with respect to each research question below. What behaviors related to COVID-19 are discussed on social media websites, like Twitter? We find that there are a wide variety of behaviors discussed on social media, including mask-wearing, hygiene (eg, handwashing), testing availability and experiences, and social distancing practices. Prior work has found evidence that mask-wearing and limited mobility were behaviors adopted to reduce disease spread during SARS [ 5 ] and that handwashing would be commonly implemented by individuals during a hypothetical pandemic influenza [ 47 ]. This prior work, however, has relied on surveys to obtain data about the behaviors that individuals implement. The use of social media to complement such work would improve both the richness and the temporal and geographic scope of the data available. Some of the identified tweets show evidence of sensitive topics. For example, we found 53 tweets related to individuals’ mental health. Prior research has found that social media can be used to identify individuals with a variety of mental health concerns, including depression [ 48 ] and suicide [ 14 ]. As there is considerable work emerging about the substantial mental health impacts of COVID-19 (eg, increases in domestic violence [ 49 ] as well as depression and anxiety [ 50 ]), this could prove to be an important avenue for future work in this field. Last, we found a small number of tweets (n=49) about vaccination related to COVID-19, of which roughly a third (n=18) showed a negative attitude. Importantly, this study was conducted prior to the authorization of any vaccines in the United States. All of the tweets considered here discuss either vaccine development or a hypothetical COVID-19 vaccine. Prior research has found similarly negative tweets during the emergence of Zika [ 51 ] and the H1N1 influenza pandemic [ 52 ]. Future work analyzing these data could provide additional insight into specific reasons that populations may be hesitant to receive the COVID-19 vaccine and could inform targeted public health messaging. How do patterns in behaviors change geospatially and temporally in the United States? As expected, the patterns in tweets classified as social distancing and shelter-in-place followed extremely similar trends. These patterns corresponded to important real-world events during the outbreak, suggesting that individuals were responding to actual events and some were describing their own personal behavior. We found, however, that tweets classified as personal mentions represented a very small subset of social distancing and shelter-in-place tweets. This is not unexpected, given that prior work has shown that personal mentions of health may be extremely uncommon [ 20 ]. How do these trends compare to other data streams, like mobility data sets, that have also shown promise in COVID-19 modeling efforts? Despite the lack of a temporal signal in tweets labeled as personal and social distancing, there was a stronger signal when comparing classified data to Descartes Labs’ mobility data. We observed meaningful regional differences between states and saw that, in general, the peak number of tweets about social distancing happened within a few weeks of the actual measured minimum in mobility. This suggests that social media data may be used as a proxy for sensor data in appropriately data-rich contexts. Recent work using geotagged Twitter data to create social networks and analyze social distancing in the context of policy decisions found similar relationships and supports this finding [ 53 ]. Limitations There are a number of limitations to consider in this work. The first is that, as mentioned above, it is known that social media data are biased in a number of ways, including demographically, and that bias differs by geographic areas [ 18 ]. Further, personal mentions of health-related information on Twitter are rare [ 19 ]. These are known limitations of using internet data and could potentially explain the variations in correlation we observed between social distancing posts and actual mobility data. Importantly, however, it is difficult to assess this without extensive prospective surveys conducted at the same time as tweet collection. Our observed wide range in correlations between the proportion of social distancing tweets and actual COVID-19 cases in individual states is an example of the ecological fallacy. State-level COVID-19 cases represent an aggregate measure of a state’s behavior, while tweets represent individual actions and observations. The available data do not allow us to probe the reasons for the variation, but a number of possible factors could be at play. Individuals’ social distancing thoughts at a specific moment in time will be influenced by contextual information about other aspects of their lives. For example, people that tweet in support of social distancing may have in-person jobs or be in high-risk groups, which could motivate them to use social media platforms to voice support for public health measures. The stronger correlation with mobility outcomes is expected by this same argument because mobility is more directly representative of individual actions. Additionally, tweeting norms could be systematically different across the country (eg, people in different states might be more or less likely to talk about social distancing based on the policies in place and the perceived threat of COVID-19). It is also possible that there are differences in which individuals use Twitter and have geolocation services enabled in different states. In an operational context, it is hugely important to combine internet data with traditional data streams in order to provide a more complete picture of an evolving scenario. Future work should focus on targeted studies to better understand potential bias. An additional known source of bias comes from imperfect classification. Our classifiers performed similarly to other classifiers used to identify health behaviors [ 15 ], but were clearly not perfect. To account for known classifier bias, we used an adjusted bootstrapping method from Daughton and Paul [ 25 ], which generates accurate confidence intervals despite classifier error. We validated our work using mobility data from Descartes Labs. However, there are a number of mobility data sources available [ 54 ]. Prior work indicates that these data have similar patterns [ 54 ], but it is possible that using a different source would produce slightly different validation results. Conclusions Behavior changes and policy decisions that occur early within an outbreak have the largest effects on disease dynamics [ 55 , 56 ]. Real-time conversations about health behaviors, in addition to other behavioral data sources such as mobility metrics or media consumption (eg, home television viewing [ 55 ]), could help improve overall knowledge and policy decisions in the early stages of an epidemic and could better capture dynamic changes caused by uncoordinated behavioral change. Using such data has the unique capability to inform public health decisions as an outbreak emerges, especially with respect to public health communication. The World Health Organization suggests a communication checklist to prepare for and minimize morbidity and mortality in the event of a pandemic [ 57 , 58 ]. The checklist emphasizes building public trust through early communication, even with incomplete information, and evaluating the impact of communication programs to assess whether recommendations are being followed. The use of social media streams as a simultaneous real-time measure of public sentiment toward messaging and a dynamic evaluation tool of communication effectiveness could be invaluable in minimizing effects from a future disease outbreak. Acknowledgments ARD, CDS, and DG created the labeling schema. DG, NP, TP, ARD, CWR, GF, and NYVC collected and analyzed the Twitter data. ARD, CDS, DG, IC, GN, and NM labeled the tweets. ARD and CWR built the supervised learning models, and ARD implemented the classifier-adjusted bootstrapped sampling. MB collected the mobility data and created several figures. ARD, CDS, and MB wrote the initial paper. All authors provided critical revisions to the paper. ARD led the project. Research support was provided by the Laboratory Directed Research and Development program of Los Alamos National Laboratory (project No. 20200721ER) and the US Department of Energy through the Los Alamos National Laboratory. 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