FATHOM OUT THE NUANCES OF SOCIAL MEDIA LITERACY AND ITS PRACTICAL ALLUSIONS TO MISINFORMATION – A REVIEW
Bibliographic record
Abstract
Digital media has become an integral part of our daily lives, connecting people from all around the world in a matter of seconds. In recent years, social media has revolutionized the flow of information and helped people to connect, share, and discuss things at their fingertips. However, this rapid exchange of information has also given rise to a concerning phenomenon – the spread of misinformation. "Misinformation" has led to a wide range of negative consequences, such as political polarization, social unrest, and erosion of trust in traditional media. This review explores the importance of social media literacy, its advantages, impacts, and effects, along with allusions to misinformation. The methodology includes a systematic search of literature in the databases from Internationally double-blinded peer-reviewed journals between 2016 and 2022, in English that also includes scientific articles. A total of 64 articles were obtained. A selection process of articles took place, applying certain inclusion and exclusion criteria, resulting in a total of 10 articles being selected. The findings signify that the conception of social media literacy has just started and more research is needed to identify its practical and theoretical implications. This is linked to the content and competencies, critical thinking, content, and context of the information, and the time and literacy level and fear of missing out on the user. Socio-emotional competencies, literacy level, and time spent stand out since social media is a frequent place of interaction between people. Thus, it is crucial to develop social media literacy skills to combat the dissemination of fake news and ensure a more responsible citizen and informed society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".