Dataset Restricted Access

Dataset for "The Good, the Bad and the Bait: Detecting and Characterizing Clickbait on YouTube"

Savvas Zannettou; Sotirios Chatzis; Kostantinos Papadamou; Michael Sirivianos

This is the dataset used for the research "The Good, the Bad and the Bait: Detecting and Characterizing Clickbait on YouTube", with DOI: 10.1109/SPW.2018.00018.

The dataset consists of three files:

1. groundtruth.json: This is the groundtruth dataset. We have 3443 manually annotated videos (we manually annotated more after the acceptance of the paper), and 17,648 videos that were obtained from channels that post clickbait or not. You can distinguish the method of annotation by observing the field "comments" in "clickbaitClassification" (the ones that have the comment "channels" are the ones obtained from the channels).

2. videos.json: Contains the data for 206K videos that were obtained as described in the paper.

3. predictions.json: It contains the mapping between the video id and the probability of our classifier. In our paper, we treat a video as clickbait if the probability is larger than 0.5.

Restricted Access

You may request access to the files in this upload, provided that you fulfil the conditions below. The decision whether to grant/deny access is solely under the responsibility of the record owner.


Anybody requesting access to the dataset must explain in the Justification section of the request the intended use of the data and agree to the following terms, including:

  1. We will not to attempt to use this data to de-anonymize, in any way, any users in this or any other dataset.
  2. We will not re-share the dataset with anyone not included in this request.
  3. We will appropriately cite the "The Good, the Bad and the Bait: Detecting and Characterizing Clickbait on YouTube" paper in any publication, of any form and kind, using this data: 

@inproceedings{zannettou2018good,
  title={ {The Good, the Bad and the Bait: Detecting and Characterizing Clickbait on YouTube} },
  author={Zannettou, Savvas and Chatzis, Sotirios and Papadamou, Kostantinos and Sirivianos, Michael},
  booktitle={2018 IEEE Security and Privacy Workshops (SPW)},
  pages={63--69},
  year={2018},
  organization={IEEE}
}


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