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[https://infodemiology.jmir.org/2021/1/e30971] - - public:weinreich
health_communication, qualitative, research, social_media - 4 | id:744667 -

Objective: In this work, we aimed to develop a practical, structured approach to identify narratives in public online conversations on social media platforms where concerns or confusion exist or where narratives are gaining traction, thus providing actionable data to help the WHO prioritize its response efforts to address the COVID-19 infodemic. Methods: We developed a taxonomy to filter global public conversations in English and French related to COVID-19 on social media into 5 categories with 35 subcategories. The taxonomy and its implementation were validated for retrieval precision and recall, and they were reviewed and adapted as language about the pandemic in online conversations changed over time. The aggregated data for each subcategory were analyzed on a weekly basis by volume, velocity, and presence of questions to detect signals of information voids with potential for confusion or where mis- or disinformation may thrive. A human analyst reviewed and identified potential information voids and sources of confusion, and quantitative data were used to provide insights on emerging narratives, influencers, and public reactions to COVID-19–related topics. Results: A COVID-19 public health social listening taxonomy was developed, validated, and applied to filter relevant content for more focused analysis. A weekly analysis of public online conversations since March 23, 2020, enabled quantification of shifting interests in public health–related topics concerning the pandemic, and the analysis demonstrated recurring voids of verified health information. This approach therefore focuses on the detection of infodemic signals to generate actionable insights to rapidly inform decision-making for a more targeted and adaptive response, including risk communication.

[https://cbail.github.io/textasdata/Text_as_Data.html?fbclid=IwAR1Nl93wTvZlhmVdifK_-I91viDfkH1R69rGwSzE2wM__OOVT_w3mJatgvI] - - public:weinreich
how_to, qualitative, quantitative, research, social_media, twitter - 6 | id:309754 -

This class covers a range of different topics that build on top of each other. For example, in the first tutorial, you will learn how to collect data from Twitter, and in subsequent tutorials you will learn how to analyze those data using automated text analysis techniques. For this reason, you may find it difficult to jump towards one of the most advanced issues before covering the basics. Introduction: Strengths and Weaknesses of Text as Data Application Programming Interfaces Screen-Scraping Basic Text Analysis Dictionary-Based Text Analysis Topic Modeling Text Networks Word Embeddings

[https://www.fsnnetwork.org/behavior-bank] - - public:weinreich
behavior_change, nutrition, qualitative, research - 4 | id:293007 -

The Food Security and Nutrition Network Behavior Bank features results from Barrier Analysis and Doer/NonDoer Studies conducted by food security and other practitioners globally. (Click here for a description of Barrier Analysis.) You can browse the database by country, region, and behavior studied to look for results for a particular area/behavior, or to look for patterns of barrier and enablers for a particular behavior or set of behaviors.

[https://www.nngroup.com/articles/usability-testing-101/?utm_source=Alertbox&utm_campaign=efe6ce0f84-UsabilityTesting_ServiceBlueprint_20191202&utm_medium=email&utm_term=0_7f29a2b335-efe6ce0f84-24361717] - - public:weinreich
design, product, qualitative, research - 4 | id:272148 -

[https://nsuworks.nova.edu/cgi/viewcontent.cgi?referer=&httpsredir=1&article=2465&context=tqr] - - public:weinreich
qualitative, research, social_marketing - 3 | id:266969 -

In this paper, we describe how PSI's qualitative research program developed from 2003 to 2013, and how using an interpretive approach and more appropriate data collection methods improved our consumer insight and marketing planning process.

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