Ben HalpernTackling Clickbait on DEV: Our Strategy and Actions
Recently, we have implemented measures to address the issue of clickbait on DEV. This post aims to explain our approach and the rationale behind it.
Defining Clickbait
Clickbait typically refers to content with certain patterns in titles, such as listicles or sensationalist headlines. While listicles, for instance, have gained traction for driving engagement on DEV, they sometimes lead to an imbalance, as illustrated by community initiatives like this Chrome add-on:
Our Approach
Emphasis on Mitigation Over Strict Filtering
Instead of implementing rigid filtering, we've opted for a more nuanced approach. The reasons are as follows:
- Balance in Content: Not all listicles are negative; they can be informative. However, they are often disproportionately rewarded due to our engagement-based system.
- Diverse Nature of Clickbait: Clickbait comes in many forms, and a rigid filter might not effectively catch them all.
- Subjectivity Issues: The subjective nature of determining what constitutes clickbait makes strict filtering impractical.
AI and Human Judgment in Scoring
We introduced a clickbait_score field in our app, ranging from 0.0 to 1.0.
Add articles clickbait_score as factor in final feed ordering
#20493
What type of PR is this? (check all applicable)
- [ ] Refactor
- [x] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
Description
This adds a new field allowing a new factor in final feed ordering. It adds the A/B tests to examine its impact on community use, and adds an API endpoint to allow labeling experimentation outside of core and the allowance of third party labeling and toolking to be created by any OSS Forem admins.
Related Tickets & Documents
QA Instructions, Screenshots, Recordings
Please replace this line with instructions on how to test your changes, a note on the devices and browsers this has been tested on, as well as any relevant images for UI changes.
Added/updated tests?
We encourage you to keep the code coverage percentage at 80% and above.
- [x] Yes
This score isn't determined solely by algorithms. We utilize AI along with human judgment to assign scores to posts, recognizing the subjective and nuanced nature of the task.
Implementing Through A/B Testing
We are carefully integrating the clickbait score. As part of our ongoing A/B tests, users may experience varying levels of clickbait mitigation. This data will help us refine our approach in the coming weeks.
Looking Ahead: Customizable User Settings
While we are working on a default setting for managing clickbait, we recognize the need for personalization. Future updates may include user-specific settings to tailor feed preferences based on clickbait scores.
We will be sharing more in the near future about a variety of strategies we are undertaking to improve the core experience and help separate signal from noise on DEV.
Happy coding ❤️
