“significant” p-value ≠ “significant” finding: The significance of statistical evidence for the true X (i.e., statistical significance of the p-value for the estimate of the true X) says absolutely nothing about the practical/scientific significance of the true X. That is, significance of evidence is not evidence of significance. Increasing your sample size in no way increases the practical/scientific significance of your practical/scientific hypothesis. “significant” p-value = “discernible” finding: The significance of statistical evidence for the true X does tell us how well the estimate can discern the true X. That is, significance of evidence is evidence of discernibility. Increasing your sample size does increase how well your finding can discern your practical/scientific hypothesis.

This manual provides a quick overview of the steps required to develop an infodemic insights report that can be used during an emergency response or for routine health programming (where so-called low-level infodemics may be more common). The steps are: 1. Choose the question that infodemic management insights could help to answer 2. Identify and select the data sources and develop an analysis plan for each data source 3. Conduct an integrated analysis across those data sources 4. Develop strategies and recommendations 5. Develop an infodemic insights report 6. Disseminate the infodemic insights report and track the actions taken.

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.

Language data There is little information available on the languages crisis-affected people speak and understand. Humanitarians often develop communication strategies without reliable data on literacy, languages spoken, or preferred means of communication. The result too often is that crisis-affected people struggle to communicate with humanitarian organizations in a language they understand. Women, children, older people, and people with disabilities are often at the greatest disadvantage because they are less likely to understand international languages and lingua francas. TWB’s Language Data Initiative addresses those issues and provides important resources for humanitarians. It supports humanitarian organizations to develop language-informed programs and communication strategies. Click on a country on the map below to see language data, resources, and maps that we have available for that country. This map will update as new data is published in the future.

A Field Guide to “Fake News” and Other Information Disorders explores the use of digital methods to study false viral news, political memes, trolling practices and their social life online. It responds to an increasing demand for understanding the interplay between digital platforms, misleading information, propaganda and viral content practices, and their influence on politics and public life in democratic societies.

The Data Playbook Beta is a recipe book or exercise book with examples, best practices, how to's, session plans, training materials, matrices, scenarios, and resources. The data playbook will provide resources for National Societies to develop their literacy around data, including responsible data use and data protection. The content aims to be visual, remixable, collaborative, useful, and informative. There are nine modules, 65 pieces of content, and a methodology for sharing curriculum across all the sectors and networks. Material has been compiled, piloted, and tested in collaboration with many contributors from across IFRC and National Societies. Each module has a recipe that puts our raw materials in suggested steps to reach a learning objective. To help support you in creating your own recipe, we also include a listing of 'ingredients' for a topic, organised by type:

research on health comm messaging effects

Version 2: https://www.youtube.com/watch?v=SYk29tnxASs Every field in science uses the same, old, wall-of-text poster design. If we can improve the knowledge transfer efficiency of that design even by a little bit, it could have massive ripple effects on all of science. Also, poster sessions tend to suck, so here's my pitch to make them more efficient AND more fun with a new approach to designing scientific posters/academic posters that is both more usable, and easier to create!

Mike Sherman and Neil Gains will present a method that allows you to create insightful, concise and practical reports in four steps, producing presentations that typically range from 15 to 25 pages.

Unfortunately for Mountain Dew The Scotsman didn’t include the fact that “chug” means “masturbation” in this particular part of the UK. And now, as Vice reports, the soft drink brand is being mercilessly ripped on Twitter for inadvertently telling everyone that they’re chronic masturbators. On Monday the company tweeted a .gif of a guy madly downing a bottle of Mountain Dew, with the slogan “epic thrills start with a chug”.

Controversy in science news accounts attracts audiences and draws attention to important science issues. But sometimes covering multiple sides of a science issue does the audience a disservice. Counterbalancing a truth claim backed by strong scientific support with a poorly backed argument can unnecessarily heighten audience perceptions of uncertainty. At the same time, journalistic norms often constrain reporters to "get both sides of the story" even when there is little debate in the scientific community about which truth claim is most valid. In this study, we look at whether highlighting the way in which experts are arrayed across truth claims-a strategy we label "weight-of-evidence reporting"-can attenuate heightened perceptions of uncertainty that can result from coverage of conflicting claims. The results of our study suggest weight-of-evidence strategies can indeed play a role in reducing some of the uncertainty audiences may perceive when encountering lop-sided truth claims.

An introduction to participatory monitoring and evaluation of communication for social change from the Communication for Social Change Consortium

Polling firm Harris Interactive has released the latest edition of an ongoing poll measuring the number of people who turn to the Internet for healthcare-related information. Analysis by Fard Johnmar of HealthCareVox.