Dataset of discussion threads from Meneame
Bibliographic record
Abstract
Dataset from our ICWSM 2017 paper. When using this resource, please use the following citation: Aragón P., Gómez V., Kaltenbrunner A. (2017) To Thread or Not to Thread: The Impact of Conversation Threading on Online Discussion, ICWSM-17- 11th International AAAI Conference on Web and Social Media, Montreal, Canada. @inproceedings {aragon2017ICWSM, author = {Arag\'on, Pablo and G\'omez, Vicen\c{c} and Kaltenbrunner, Andreas}, title = {To Thread or Not to Thread: The Impact of Conversation Threading on Online Discussion}, booktitle = {ICWSM-17 - 11th International AAAI Conference on Web and Social Media}, publisher = {The AAAI Press}, location = {Montreal, Canada}, year = 2017 } More info about this dataset can also be found at: Aragón P., Gómez V., Kaltenbrunner A., (2017) Detecting Platform Effects in Online Discussions, Policy & Internet, 9, 2017. @article{aragon2017PI, author = {Arag\'on, Pablo and G\'omez, Vicen\c{c} and Kaltenbrunner, Andreas}, title = {Detecting Platform Effects in Online Discussions}, journal = {Policy \& Internet}, volume = {9}, number = {4}, pages = {420-443}, doi = {10.1002/poi3.158}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/poi3.158}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/poi3.158}, year = {2017} } Crawling process We built a crawling process that collects all the stories in the front page of Meneame from 2011 to 2015 (both years included). We then performed a second crawling process to collect every comment from the discussion thread of each story. From both crawling processes, we obtained 72,005 stories and 5,385,324 comments. It is important to highlight two issues taken into account when the crawler was designed. First, the machine-readable robots.txt file on Meneame does not disallow this process. Second, the footnote of Meneame indicates the licenses of the code, graphics and content of the website. The license for content is Attribution 3.0 Spain (CC BY 3.0 ES) which allows us to release this dataset. Fields Every discussion thread is stored in a JSON file named with the URL slug of the corresponding story in Meneame, located in a yyyy-mm-dd folder. The JSON file is an array of elements with the following fields: id (string): ID of the story/comment sent (timestamp): Date of the story/comment as yyyy-MM-ddThh:mm:ssZ. message (string): Text of the story/comment user (string): Username of the authoring story/comment karma (number): Karma score of the comment when the crawling was performed comments_count (number): Number of comments in reply to the story/post votes (number): Number of votes to the story/comment thread (string): URL of the thread thread_id (string): Sequential arriving order to the thread (0 if story, >=1 if comment) depth (string): Depth within the thread (0 if story, >=1 if comment) url (string): URL of the specific story/comment title (string): Title, only available for stories. published (string): Date when published on the front page, only available for stories. tags (string): Tags, only available for stories. clics (string): Number of clicks, only available for stories. users (string): Number of user votes, only available for stories. anonymous (string): Number of anonymous votes, only available for stories. negatives (string): Number of negative votes, only available for stories. in_reply_to_id (string): ID of the parent story/comment, only available for comments. in_reply_to_user (string): Authoring user of the parent story/comment, only available for comments. in_reply_to_thread_id (string): Sequential arriving order to the thread of of the parent story/comment, only available for comments. Acknowledgment This work is supported by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Programme (MDM-2015-0502).
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.043 |
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".