Show HN: An Open Source Tool to Combat Clickbait Links
11–20 of 29 posts
Re: Show HN: An Open Source Tool to Combat Clickbait Links
#12So that means it's fetching the content (or at least the HTML portion) of every eligible link? That seems like potentially a lot of network traffic for what it does. Find if you have a fast connection, I suppose.
Re: Show HN: An Open Source Tool to Combat Clickbait Links
#13How does your approach of looking for text frequency compared to, say, pattern-matching existing clickbait titles from a database? Can the two approaches be combined (say, by using Splice to generate a corpus, remove false positives manually, then use it to train a pattern matcher?) Not having to load the linked article has huge benefits on bandwidth, robustness, etc.
Re: Show HN: An Open Source Tool to Combat Clickbait Links
#14Re: Show HN: An Open Source Tool to Combat Clickbait Links
#15Not sure how to account for that. Just wanted to point out the lossiness of the algorithm.
Looks like a cool app. Thanks!
Re: Show HN: An Open Source Tool to Combat Clickbait Links
#16Re: Show HN: An Open Source Tool to Combat Clickbait Links
#17BBC isn't as reliable as its reputation would have you believe ....
Re: Show HN: An Open Source Tool to Combat Clickbait Links
#18Not an issue at all but I am curious. Why "Spanish" has the Mexico flag? Was the spanish language modeled with that specific variant?
Re: Show HN: An Open Source Tool to Combat Clickbait Links
#19Be interesting to train a model using user generated, is this click bait title yes/no. Is this something machine learning could get very good at?
There are definitely training sets paired with Bayesian inference that attempt to do this.
Re: Show HN: An Open Source Tool to Combat Clickbait Links
#20I'm not sure if this can be done through an extension, but an alternative way to go about this is by measuring time spent on the page. If the page has X words which takes Y minutes to read, but a certain number of people bounce before that time, the score is lowered. The more users that stay on the page for Y minutes, the higher the score.
There are a lot of assumptions in that solution, but it might be worth considering.