Social and Non Social Media Users During The COVID-19 Pandemic Confinement Period in Canada: The "Plugged-In", Unplugged and Other Population Segments
Bibliographic record
Abstract
Canadians are using a variety of social and non-social media vehicles to gather information, share experiences and express anxieties during the COVID-19 confinement period. The purpose of the study is to produce a portrait of media use in Canada, paying special attention to the typical population segments in the Canadian population differentiated by their media vehicles and sources of information about the pandemic. The study used as its data source a survey sample of 4,600 adult Canadians aged 15 years old and over during the period of July 20-26 2020, and collected by Statistics Canada. Media user activities comprised a set of 11 dichotomous scales collecting data on main sources of information such as social media posts, online news, online magazines, video platforms, e-mails as well as non internet-based sources. A market segmentation analysis of these scales using Principal Components and k-means cluster analysis revealed the presence of six major population segments: Social Media Buffs (27%), News Followers (33%), Unplugged (10%),Plugged-In (9%), E-Mailers (7%) and Mixed Source Users (16%). The segment mottos were as follows: "Social Media Influencers Know Their Stuff!", "Track Those Headlines!", "I’ve Got My Own Info Sources About The Pandemic!", "Did You Read The Last Blog?" "My People Know Better!" and "Better Info Means More Choices!". This study suggests that media users in Canada constitute a very diverse group of individuals who are engaged in social and non social media to obtain timely information about the pandemic. However, they can also be exposed to inaccurate, misleading information about the virus, its transmission and its treatments. In this light, market segmentation may be a useful tool for decision makers to categorize population members by their typical attitudinal traits and, by doing so, facilitate better public campaigns directed at population segments, help design messages, and implement changes that can promote more efficient ways to deal with their target audiences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".