{"url":"/dataset/sepehr-rumtel01","name":"Sepehr_RumTel01","full_name":null,"description_markdown":"The expansion of social networks has accelerated the transmission of information and news at every communities. Over the past few years, the number of users, audiences and social networking publishers, are increased dramatically too. Among the massive amounts of information and news reported on these networks, we are faced with issues that have not been verified which is called “rumors”. Identifying rumors on social networks is carried out in the form of rumor detection approaches; the massive amount of these news and information force to use the machine learning techniques. The most important problem with auto-detection approaches is the lack of a database of rumors. For that matter, in this article, a collection of rumors published on the social network “telegrams” have been collected. These data are gathered from five Persian-language channels that have specially reviewed this issue. The collected data set contains 3283 messages with 2829 attachments, having a volume of over 1.6 gigabytes. This dataset can also be used for different purposes of natural language processing.","description_withheld":null,"homepage":"https://doi.org/10.17632/jw3zwf8rdp","introduced_date":"2019-01-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-speech-act-classifier-for-persian-texts-and","title":"A Speech Act Classifier for Persian Texts and its Application in Identifying Rumors","first_author":"Zoleikha Jahanbakhsh-Nagadeh","url":null},"license":{"name":"CC BY NC 3.0","url":null},"modalities":[],"tasks":[{"name":"Rumour Detection","url":"/task/rumour-detection","datasets_with_task":"/datasets/task/rumour-detection"}],"languages":[{"name":"Persian","url":"/datasets/language/persian"}],"variants":["Sepehr_RumTel01"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/rumour-detection-on-sepehr-rumtel01","task":"Rumour Detection","dataset_variant":"Sepehr_RumTel01","rows":4,"metrics":["F-Measure"],"first_row_in_archive_order":{"model":"ParsBERT+PCapsNet +SA+Title+ Auxiliary","paper":"/paper/a-deep-content-based-model-for-persian-rumor","metrics":{"F-Measure":"0.947"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-deep-content-based-model-for-persian-rumor","title":"A Deep Content-Based Model for Persian Rumor Verification","date":"2020-11-29","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-semi-supervised-model-for-persian-rumor","title":"A semi-supervised model for Persian rumor verification based on content information","date":"2020-11-20","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-model-to-measure-the-spread-power-of-rumors","title":"A Model to Measure the Spread Power of Rumors","date":"2020-02-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-speech-act-classifier-for-persian-texts-and","title":"A Speech Act Classifier for Persian Texts and its Application in Identifying Rumors","date":"2019-01-12","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}