Great video about listening to music "professionally"

It's about emotion & intent (context) over the theory, usually.

Caulfield, T. (2017, October 24). The Vaccination Picture by Timothy Caulfield. Penguin Random House Canada. https://www.penguinrandomhouse.ca/books/565776/the-vaccination-picture-by-timothy-caulfield/9780735234994

Polis. (n.d.). Retrieved April 26, 2022, from https://pol.is/home

Continuing with the idea above about the shift of Sunday morning talk shows and the influence of Hard Copy, is social media exerting a negative influence on mainstream content and conversation as a result of their algorithmic gut reaction pressure? How can we fight this effect?

https://defector.com/

From "former staffers of Deadspin".

http://puck.news/who-is-puck/

Another platform play of journalists banding together to find a niche space of readers.

Fischer, O., Jeitziner, L., & Wulff, D. U. (2021). Affect in science communication: A data-driven analysis of TED talks. PsyArXiv. https://doi.org/10.31234/osf.io/28yc5

Wise, J. (2021). Headlines play down the gravity of covid-19 in children. BMJ, 375, n2826. https://doi.org/10.1136/bmj.n2826

Linda Clauson. (2021, November 6). Join us for the Scope and Scale of Online Intimidation: How social media is a tool for both supporting and disrupting the circulation of credible info and analysis. With @CaulfieldTim, @whkchun @gruzd @JuliaMWrightDal Register here: Https://events.myconferencesuite.com/RSC_COEE2021/reg/landing https://t.co/SY4ZjGF2Me [Tweet]. @lindaz_clauson. https://twitter.com/lindaz_clauson/status/1457067508171780105

How online misinformation spreads. (n.d.). Retrieved October 19, 2021, from https://knowablemagazine.org/article/society/2021/how-online-misinformation-spreads

Iacobucci, G. (2021). Covid and flu: What do the numbers tell us about morbidity and deaths? BMJ, n2514. https://doi.org/10.1136/bmj.n2514

Akhther, N. (2021). Internet Memes as Form of Cultural Discourse: A Rhetorical Analysis on Facebook. PsyArXiv. https://doi.org/10.31234/osf.io/sx6t7

Lutkenhaus, R. O., Jansz, J., & Bouman, M. P. A. (2019). Mapping the Dutch vaccination debate on Twitter: Identifying communities, narratives, and interactions. Vaccine: X, 1. https://doi.org/10.1016/j.jvacx.2019.100019

Yang, K.-C., Pierri, F., Hui, P.-M., Axelrod, D., Torres-Lugo, C., Bryden, J., & Menczer, F. (2020). The COVID-19 Infodemic: Twitter versus Facebook. ArXiv:2012.09353 [Cs]. http://arxiv.org/abs/2012.09353

Smith, N., & Graham, T. (2019). Mapping the anti-vaccination movement on Facebook. Information, Communication & Society, 22(9), 1310–1327. https://doi.org/10.1080/1369118X.2017.1418406

Krupenkin, Masha, Kai Zhu, Dylan Walker, and David M. Rothschild. ‘If a Tree Falls in the Forest: COVID-19, Media Choices, and Presidential Agenda Setting’. SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, 22 September 2020. https://doi.org/10.2139/ssrn.3697069.

Darren Dahly. (2019, September 4). It seems appropriate to do a thread on our recent session about the use of Twitter by statisticians. Https://t.co/eFwLDuXnOU [Tweet]. @statsepi. https://twitter.com/statsepi/status/1169313702715281408

Sanders, J., Tosi, A., Obradović, S., Miligi, I., & Delaney, L. (2021). Lessons from lockdown: Media discourse on the role of behavioural science in the UK COVID-19 response. PsyArXiv. https://doi.org/10.31234/osf.io/dw85a

Burel, Gregoire; Farrell, Tracie; Mensio, Martino; Khare, Prashant and Alani, Harith (2020). Co-Spread of Misinformation and Fact-Checking Content during the Covid-19 Pandemic. In: Proceedings of the 12th International Social Informatics Conference (SocInfo), LNCS.

Ali, A. (2020, August 28). Visualizing the Social Media Universe in 2020. Visual Capitalist. https://www.visualcapitalist.com/visualizing-the-social-media-universe-in-2020/

Embracing the slowdown. (n.d.). Retrieved August 30, 2020, from https://marketing.twitter.com/emea/en_gb/insights/embracing-the-slowdown

Arqoub, O. A., Elega, A. A., Özad, B. E., Dwikat, H., & Oloyede, F. A. (2020). Mapping the Scholarship of Fake News Research: A Systematic Review. Journalism Practice, 0(0), 1–31. https://doi.org/10.1080/17512786.2020.1805791

Walter, N., Brooks, J. J., Saucier, C. J., & Suresh, S. (2020). Evaluating the Impact of Attempts to Correct Health Misinformation on Social Media: A Meta-Analysis. Health Communication, 0(0), 1–9. https://doi.org/10.1080/10410236.2020.1794553

COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved 31 July 2020, from https://covid-19.iza.org/publications/dp13388/

Cotti, C. D., Engelhardt, B., Foster, J., Nesson, E. T., & Niekamp, P. S. (2020). The Relationship between In-Person Voting and COVID-19: Evidence from the Wisconsin Primary (Working Paper No. 27187; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27187

Bhattacharya, C., Chowdhury, D., Ahmed, N., Ozgur, S., Bhattacharya, B., Mridha, S. K., & Bhattacharyya, M. (2020). The Nature, Cause and Consequence of COVID-19 Panic among Social Media Users in India. https://doi.org/10.31234/osf.io/dgr45

Motta, M., Stecula, D., & Farhart, C. E. (2020). How Right-Leaning Media Coverage of COVID-19 Facilitated the Spread of Misinformation in the Early Stages of the Pandemic [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/a8r3p

Krumpal, I. (2020). Soziologie in Zeiten der Pandemie [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/yqdsu

COVID-19 Social Science Tracker - Google Sheets

Register here: (n.d.). Google Docs. Retrieved May 5, 2020, from https://docs.google.com/forms/d/e/1FAIpQLSdqXWlf0sbRR9wSH_42shm4vU4tHcCe0bQZuC-6ngHaI4I32w/viewform??embedded=true&usp=embed_facebook

Using LinkedIn for Social Research. (2019, July 9). Impact of Social Sciences. https://blogs.lse.ac.uk/impactofsocialsciences/2019/07/09/using-linkedin-for-social-research/

Velásquez, N., Leahy, R., Restrepo, N. J., Lupu, Y., Sear, R., Gabriel, N., Jha, O., Goldberg, B., & Johnson, N. F. (2020). Hate multiverse spreads malicious COVID-19 content online beyond individual platform control. ArXiv:2004.00673 [Nlin, Physics:Physics]. http://arxiv.org/abs/2004.00673

Cinelli, M., Morales, G. D. F., Galeazzi, A., Quattrociocchi, W., & Starnini, M. (2020). Echo Chambers on Social Media: A comparative analysis. ArXiv:2004.09603 [Physics]. http://arxiv.org/abs/2004.09603

Bail, C. A. (2016). Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media. Proceedings of the National Academy of Sciences, 113(42), 11823–11828. https://doi.org/10.1073/pnas.1607151113

Golino, H., Christensen, A. P., Moulder, R. G., Kim, S., & Boker, S. M. (2020, April 14). Modeling latent topics in social media using Dynamic Exploratory Graph Analysis: The case of the right-wing and left-wing trolls in the 2016 US elections. https://doi.org/10.31234/osf.io/tfs7c

Correia, Rion Brattig, Ian B. Wood, Johan Bollen, and Luis M. Rocha. “Mining Social Media Data for Biomedical Signals and Health-Related Behavior.” Annual Review of Biomedical Data Science, May 4, 2020. https://doi.org/10.1146/annurev-biodatasci-030320-040844.

Ferres, L. (2020 April 10). COVID19 mobility reports. Leo's Blog. https://leoferres.info/blog/2020/04/10/covid19-mobility-reports/

Vilella, S., Paolotti, D., Ruffo, G. et al. News and the city: understanding online press consumption patterns through mobile data. EPJ Data Sci. 9, 10 (2020). https://doi.org/10.1140/epjds/s13688-020-00228-9

Jarynowski, A., Wójta-Kempa, M., & Belik, V. (2020, April 22). TRENDS IN PERCEPTION OF COVID-19 IN POLISH INTERNET. https://doi.org/10.31234/osf.io/dr3gm

Alam, F., Sajjad, H., Imran, M., & Ofli, F. (2020). Standardizing and Benchmarking Crisis-related Social Media Datasets for Humanitarian Information Processing. ArXiv:2004.06774 [Cs]. http://arxiv.org/abs/2004.06774

Analysis of a subreddit for Trump supporters, based on comparisons of the users of various subreddits.

And references to Granovetter, with his famous 1973 article. This [article on sports media and Twitter](http://www.cabdirect.org/abstracts/20143219270.html sounds contextually relevant.