Revised URL: https://about.twitter.com/content/dam/about-twitter/company/twitter-for-good/en/ngo-handbook-digital.pdf
a measurement instrument for evaluating susceptibility to seven social influence principles, namely social learning, social comparison, social norms, social facilitation, social cooperation, social competition, and social recognition
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.
New research suggests that employees with a diverse Twitter network — one that exposes them to people and ideas they don’t already know — tend to generate better ideas.
Social networks provide a powerful approach for health behavior change. This article documents how social network interventions have been successfully utilized for a range of health behaviors including HIV risk practices, smoking, exercise, dieting, family planning, bullying, and mental health. We review the literature that suggests relationship between health behaviors and social network attributes demonstrate a high degree of specificity. The article then examines hypothesized social influence mechanisms including social norms, modeling, and social rewards and the factors of social identity and social rewards that can be employed to sustain social network interventions. Areas of future research avenues are highlighted, including the need to examine and analytically adjust for contamination and social diffusion, social influence versus differential affiliation, and network change. Use and integration of mhealth and face-to-face networks for promoting health behavior change are also critical research areas.
resources for mapping, assessing and weaving networks
For review, “dark testing” is A/B testing on Facebook conducted by 1) building multiple variations of a single post by adjusting the message, thumbnail, image, etc., 2) serving these variations to different, similar audiences, and 3) measuring performance and designating a “winner.”