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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://www.nngroup.com/articles/social-media-research-insights/?utm_source=Alertbox&utm_campaign=2b91975c02-EMAIL_CAMPAIGN_2020_11_12_08_52_COPY_01&utm_medium=email&utm_term=0_7f29a2b335-2b91975c02-24361717] - - public:weinreich
design, social_media, social_network, strategy - 4 | id:706903 -

Companies should experiment with interactive social media content types, include relevant calls to action in posts, and avoid posting too frequently.

[https://therealalexa.com/accessible-social] - - public:weinreich
design, graphic_design, social_media, target_audience - 4 | id:573776 -

Accessibility on Social Media So you want to be more inclusive online? Excellent! Whether you're looking to improve your personal social media or accounts that you manage professionally, there are a lot of basic best practices you can implement to make your online presence more accessible. Ultimately, this makes a big impact on the experience that users with vision and/or hearing disabilities have on social media. Below you will find tips, tricks, and information on digital accessibility. These resources are by no means exhaustive, but are a good starting place for creating accessible and more inclusive social media content. I've also put together a quick and handy checklist to help you double-check the content you create for common accessibility pitfalls.

[https://www.thinkwithgoogle.com/marketing-strategies/app-and-mobile/social-media-strategy/?utm_source=twitter&utm_medium=social&utm_campaign=drumbeat&utm_term=AppMobile&utm_content=11192020&linkId=104829373] - - public:weinreich
social_media, strategy - 2 | id:438220 -

our rule of thumb is this: When more than 20% of comments are off-topic or hostile, it's time to pivot and introduce a new creative message.

[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

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