Health media collaboratory9/26/2023 ![]() If our lens is appropriately focused, we can identify content of interest and avoid collecting a lot of irrelevant information. As such, search filters are the lens through which we can observe what and how people communicate. Social media data collection in infodemiology is usually defined by the keywords and search filters used to retrieve data from the platform. Yet despite the rich potential of these platforms for research and analysis, methods for collecting, cleaning, and reporting social media data can vary widely, making the evaluation and comparison of studies using those data difficult at best. A simple search of any social media platform can provide a tantalizing bounty of information. Similarly, YouTube has millions of active users who view, post, rate, and comment on its rich video content and advertising. Metadata fields describe the reach and patterns of the diffusion of a given message, along with some limited characteristics of the users posting messages. In contrast, Twitter, which is by nature a much more public-facing platform, has millions of active users who provide rich qualitative data in the content of microblog messages (tweets) as well as important quantitative data embedded in the metadata. While Facebook remains the most commonly used social media platform, varying privacy settings and complex application programming interface (API) streams make the collection and interpretation of Facebook data for observational research extremely challenging. As people increasingly turn to social media for news and information, these platforms can serve as novel sources of observational data for infodemiology, public health surveillance (infoveillance, digital disease detection), tracking health attitudes and behavioral intention, and measuring community-level psychological characteristics related to health outcomes. The Internet and social media have quickly become major sources of health information, providing both broad and targeted exposure to such information as well as facilitating information-seeking and sharing. Anytime they become available, we try to to collect data there.Social media have transformed public and interpersonal communications. It's a rapidly-changing environment for social data. ![]() And Foursquare is all about geolocation, so we're really excited about working with that. Glen: We collect from a variety of social media platforms, Tumblr, Twitter, Facebook, You Tube, and WordPress. Tableau: What sort of data are you looking at? It works very well with the metadata variables on the backside of a tweet. You can map out where the tweets are into these great cluster circles. But at the back end of a tweet, there could be 20 to 25 different types of metadata.Īnd Tableau is great with tweets. With the front end of a tweet, there are about four sources of information that you can get. So you really need to clean your social media data before you even put it into Tableau. You need to know that the tweet you're looking at is the behavior that you're trying to study. And just because you use a key word, you can't assume that your question that you're asking is going to be contained in a tweet. Glen: The first thing you need to realize is social data is dirty data. Tableau: If I’m new to analyzing social data, what should I keep in mind? Any tips for working with social data in Tableau? So we want to study that data, harness that data, and use it to make people healthier. And those messages can lead us to making really bad health choices. ![]() We get inundated with these data messages. Over the last ten years, with the advent of the Internet, the advent of social media, we now have screens in front of us at all times. Glen Szczypka, Deputy Director, Health Media Collaboratory: Our mission is data for public good. Tableau: Can you describe a little bit about what you do at the Health Media Collaboratory? Reference Materials Toggle sub-navigation.Teams and Organizations Toggle sub-navigation.Plans and Pricing Toggle sub-navigation. ![]()
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