Plateforme pour se proteger tant de soi-meme que de ses amis sur facebook
Bibliographic record
Abstract
Social networks deal every day with millions of users (individuals or companies). They are directly affected by their rapid expansion. Some have developed a certain dependence on the use of social networks and even transform their everyday lifestyle. However, this craze for social networking is not always secure. It is obvious that their expansion promotes and serves the increase of online attacks. Social networks are an ideal opportunity for criminals and fraudsters to take advantage of users. They give access to millions of potential victims. Threats coming from “friends” on social networks are numerous: cyberintimidation, fraud, criminal harassment, moral and physical threats, incitement to suicide, circulation of compromising contents, hatred promotions, etc. There is also a “very close friend” who could cause us problems with his behavior on social networks: ourselves. When a user discloses too much information about himself, it contributes unwittingly to attracting scammers who are continually looking for preys. This thesis presents a new approach to protect Facebook users. We created a platform based on two systems: Protect_U and Protect_UFF. The first system tries to protect users from themselves by analysing the content of their profiles and by suggesting a list of recommendations in order to reduce the publication of private information. The second system aims to protect users from their “friends” who have profiles presenting alarming symptoms (psychopaths, fraudsters, criminals, etc.) taking into account essentially three main parameters: narcissism, lack of emotions and aggressive behaviour.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.226 | 0.160 |
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".