rumdetect2017

Datasets========The main directory contains the directories of two Twitter datasets: twitter15 and twitter16. In each directory, there are:- 'tree' sub-directory: This folder contains all the tree files, each of which corresponds to the tree structure given a source tweet whose file name is indicated by the source tweet ID. In the tree file, each line represents an edge given in the following format: ** parent node -&gt; child node ** Each node is given as a tuple: ['uid', 'tweet ID', 'post time delay (in minutes)'] - label.txt file: This file provides the ground-truth labels of the trees in a format like: ** 'label:source tweet ID' - source_tweets.txt file: This file provides the source posts content of the trees in a format like: ** 'source tweet ID \t source tweet content' <br>Note that constrained by the terms of Twitter service, we cannot contain the content of the rest of the tweets. Data users can obtain the sepcifics based on the provided tweet IDs and uids by their own.<br>Feature description===================- Content features: uni-grams, bi-grams (presence/absence, binary)- User features: ** # of followers ** # of friends ** ratio of followers and friends ** # of history tweets ** registration time (year) ** whether a verify account or not<br>References==========Substantial number of source tweets and their correspoding propagations trees were extracted based on two reference datasets described and released by the following works:<br>- twitter15: @inproceedings{liu2015real, title={Real-time Rumor Debunking on Twitter}, author={Liu, Xiaomo and Nourbakhsh, Armineh and Li, Quanzhi and Fang, Rui and Shah, Sameena}, booktitle={Proceedings of the 24th ACM International on Conference on Information and Knowledge Management}, pages={1867--1870}, year={2015} }<br>- twitter16: @inproceedings{ma2016detecting, title={Detecting Rumors from Microblogs with Recurrent Neural Networks}, author={Ma, Jing and Gao, Wei and Mitra, Prasenjit and Kwon, Sejeong and Jansen, Bernard J. and Wong, Kam-Fai and Meeyoung, Cha}, booktitle={The 25th International Joint Conference on Artificial Intelligence}, year={2016}, organization={AAAI} } If you use the datasets released hereby, please also cite:<br> @inproceedings{ma2017detect, title={Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning}, author={Ma, Jing and Gao, Wei and Wong, Kam-Fai}, booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, volume={1}, pages={708--717}, year={2017} }<br><br><br>数据集说明 主目录包含两个推特(Twitter)数据集的子目录：twitter15与twitter16。每个子目录下包含以下内容： - `tree` 子文件夹：该文件夹存储所有传播树文件，每个文件对应一条源推文(source tweet)的传播树结构，文件名由该源推文的ID命名。传播树文件中每行代表一条边，格式为**父节点 -> 子节点**。每个节点以元组形式表示为：['uid', '推文ID', '发布延迟时长（单位：分钟）'] - `label.txt` 文件：该文件提供各传播树的真值标签，格式为**`标签:源推文ID`** - `source_tweets.txt` 文件：该文件存储各传播树对应的源推文内容，格式为**`源推文ID 源推文内容`** 注意：受推特(Twitter)服务条款限制，本数据集未包含其余推文的内容。数据使用者可自行通过提供的推文ID与用户ID获取相关细节。 特征说明 - 内容特征：一元语法、二元语法（存在/不存在形式，二值化） - 用户特征：**粉丝数量**、**关注数量**、**粉丝关注比**、**历史推文总数**、**注册年份**、**是否为认证账号** 参考文献 本数据集的大量源推文及其对应传播树，基于以下两项已发表并公开的参考数据集提取： - twitter15：@inproceedings{liu2015real, title={Real-time Rumor Debunking on Twitter}, author={Liu, Xiaomo and Nourbakhsh, Armineh and Li, Quanzhi and Fang, Rui and Shah, Sameena}, booktitle={Proceedings of the 24th ACM International on Conference on Information and Knowledge Management}, pages={1867--1870}, year={2015} } - twitter16：@inproceedings{ma2016detecting, title={Detect Rumors from Microblogs with Recurrent Neural Networks}, author={Ma, Jing and Gao, Wei and Mitra, Prasenjit and Kwon, Sejeong and Jansen, Bernard J. and Wong, Kam-Fai and Meeyoung, Cha}, booktitle={The 25th International Joint Conference on Artificial Intelligence}, year={2016}, organization={AAAI} } 若您使用本数据集，请同时引用以下论文： @inproceedings{ma2017detect, title={Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning}, author={Ma, Jing and Gao, Wei and Wong, Kam-Fai}, booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, volume={1}, pages={708--717}, year={2017} }