788 research outputs found
sj-docx-1-bds-10.1177_20539517211041279 - Supplemental material for Different types of COVID-19 misinformation have different emotional valence on Twitter
No full textSupplemental material, sj-docx-1-bds-10.1177_20539517211041279 for Different types of COVID-19 misinformation have different emotional valence on Twitter by Marina Charquero-Ballester, Jessica G Walter, Ida A Nissen and Anja Bechmann in Big Data & Society</p
sj-docx-1-nms-10.1177_14614448221122146 – Supplemental material for Digital false information at scale in the European Union: Current state of research in various disciplines, and future directions
No full textSupplemental material, sj-docx-1-nms-10.1177_14614448221122146 for Digital false information at scale in the European Union: Current state of research in various disciplines, and future directions by Petra de Place Bak, Jessica Gabriele Walter and Anja Bechmann in New Media & Society</p
DATA EVERYWHERE: HIDING INFORMATION IN FACEBOOK GROUPS
No full textUser data mining is a necessary and desirable commodity to most internet companies and in highly interoperable services where data flows across companies and platforms there seem to be no boundaries for data mining. The largest, most interoperable, and diversified social media data company on the internet is Facebook. The company collects personal and sensitive data about the user across devices and services in various contexts ranging from activity on Facebook to behavior on external websites, pictures from Instagram, playlists from Spotify, and running routes in Runkeeper. The users share these data paths of their lives with their network of friends, Facebook, external companies, advertising agencies and app developers. Bechmann (2015) shows that young Danes use especially Facebook groups as their primary privacy filter to avoid information being shared across services and with all their Facebook friends. The aim of this paper is to analyze what kind of data is shared on Facebook with a focus on open, closed and secret Facebook groups. Methodologically, the paper primarily draws on API Facebook wall and group data retrieval and interviews with 8 Danish high school students and 1 American college student between 18-20 years old. The study will draw perspectives to preliminary results from a broad Facebook API study of 1000 Danes (sample mirroring the Danish population demographics) in the conference presentation. The analysis will make use of content analysis as a combination of manual coding and software supported counting and classification of words according to topics
The Dynamics of Real-Time Contentious Politics:How ubiquitous internet shapes and transforms popular protest in China
No full textKeeping it Real: From Faces and Features to Social Values in Deep Learning Algorithms on Social Media Images
No full textThis paper wants to supplement computational tests of deep learning vision algorithms with a sociologically grounded performance test of three widely used vision algorithms on Facebook images (Clarifai, Google Vision and Inception-v3). \ \ The test shows poor results and the paper suggests the use of a two-level labeling model that combines features with theoretically inspired accounts of the social value of pictures for uploaders. The paper contributes a suggestion for labeling categories that connects the two levels, and in conclusion discusses both advantages and disadvantages in accelerating user profiling through a better understanding of the incentives to upload images in the data-driven algorithmic society
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