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Twitter15 dataset

GitHub - OwenLeng/rumor-detection-include-twitter15

  1. Datasets. The main directory contains the directories of Weibo dataset and two Twitter datasets: twitter15 and twitter16. In each directory, there are: twitter15.train, twitter15.dev, and twitter15.test file: This files provide traing, development and test samples in a format like: 'source tweet ID \t source tweet content \t label'
  2. Datasets. The main directory contains the directories of Weibo dataset and two Twitter datasets: twitter15 and twitter16. In each directory, there are: twitter15.train, twitter15.dev, and twitter15.test file: This files provide traing, development and test samples in a format like: 'source tweet ID \t source tweet content \t label
  3. Raw Twitter Datasets. The RAW Twitter datasets is provided in CSV and JSON formats with information directly lifted from Twitter's servers. The unprocessed twitter datasets contains tweet ids and user ids which the end user of the content can then rehydrate (i.e. request the full Tweet, user, or Direct Message content) using the Twitter APIs

GitHub - chunyuanY/RumorDetectio

detection datasets Twitter15/16, illustrating the benefits of graphical modelling. However, our models are prone to overfitting on those small datasets, and there is room for further improvements. Finally, our work highlights the need for better datasets for Fake News detection on social media.1 1 Introductio 7 Clicbait Dataset 1: Source URL: github.com [ Local copy ] Creator (s): Abhijnan Chakraborty, Bhargavi Paranjape, Sourya Kakarla, and Niloy Ganguly. In: Stop clickbait: Detecting and preventing clickbaits in online news media ter datasets demonstrate that our recursive neural models 1) achieve much better per-formance than state-of-the-art approaches; 2) demonstrate superior capacity on de-tecting rumors at very early stage. 1 Introduction Rumors have always been a social disease. In re-cent years, it has become unprecedentedly conve Jing Ma (CUHK) 2018/7/15 1 Rumor Detection on Twitter with Tree-structured Recursive Neural Networks Jing Ma1, Wei Gao2, Kam-Fai Wong1,3 1The Chinese University of Hong Kong 2Victoria University of Wellington, New Zealand 3MoE Key Laboratory of High Confidence Software Technologies, China July 15-20, 2018-ACL 2018@ Melboume, Australi

will reproduce the average experimental results of 100 iterations of BiGCN model on Twitter15 dataset with 5-fold cross-validation. If you find this code useful, please let us know and cite our paper. If you have any question, please contact Tian at: bt18 at mails dot tsinghua dot edu dot cn In each dataset, tweets and their associated retweets and user response comments are included. Twitter15 and Twitter16 contain 1490 and 818 source tweet posts respectively. Four different rumour labels are applied with these two datasets, including True Rumour (TR), Non-Rumour (NR), False Rumour (FR) and Unverified Rumour (UR)

Twitter Dataset Download - TrackMyHashtag

These datasets is collected tweets, response tweets (replies and retweets), and propagation structure on Twitter. The source tweets in Twitter15 and Twitter16 datasets are annotated with one of the four class labels: nonrumor, true rumor, false rumor, and unverified rumor Twitter15 Twitter16 PHEME SemEval #source tweets 1,490 818 6,425 446 #all tweets 624,458 363,535 105,354 42,195 We use a public COVID-19 Twitter dataset [21] for our analyses.10 We use version 4 of the dataset, which contains tweets from 1st January 2020 to 5th April 2020 The datasets used in the experiment are two publicly available datasets Twitter15 and Twitter16 . Each of the two datasets is divided into five subsets. More specifically, Twitter15 is divided into five subsets denoted by Twitter150,..., Twitter154 respectively, with each subset further divided into a train dataset and a test dataset.Twitter16 is divided in a similar manner

ple of reference datasets, namely Twitter15 (Liu. et al., 2015) and T witter16 (Ma et al., 2016). The. original datasets were released and used for binary. classification of rumor and non-rumor. Figure 2: Results of early fake news detection on the Twitter15, T witter16 and Weibo dataset. By changing the time delays, the accuracy of several competiti ve models is shown in Figure 2. In 0 to Files in the dataset include: 1. Ball_by_Ball : Includes ball by ball details of all the 577 matches. 2. Match : Match metadata. 3. Player : Player metadata. 4. Player_Match : to know , who is the captain and keeper of the match , Includes every player who take part in match even If player haven't get a chance to either bat or bowl We evaluate our method on three large-scale datasets, Twitter15/16 and Weibo. Experimental results demonstrate the superiority of the proposed method. The rest part of this paper is organized as follows: Section 2 presents a detailed description of the related works of this paper

Datasets - 114.215.172.15

Jing Ma (CUHK) Results on Early Detection In the first few hours, the accuracy of the kernel-based methods climbs more rapidly and stabilize more quickly cPTK can detect rumors with 72% accuracy for Twitter15 and 69.0% for Twitter16 within 12 hours, which is much earlier than the baselines and the mean official report times 2016/7/1 25 (a) Twitter15 DATASET (b) Twitter16 DATASET With two arguments, first stands for dataset's name, the latter is the name of the model ('GCN','GAE','VGAE' can be chosen) python Model_Twitter.py Twitter15 VGAE Resul Interpretable Rumor Detection in Microblogs by Attending to User Interactions. 29 Jan 2020 · Ling Min Serena Khoo , Hai Leong Chieu , Zhong Qian , Jing Jiang ·. Edit social preview. We address rumor detection by learning to differentiate between the community's response to real and fake claims in microblogs. Existing state-of-the-art models. experimental datasets, and split the dataset into training, valida-tionandtestsetswith70%,10% Twitter15 Twitter16 Model F1 Recall PrecisionAccuracy F1 Recall PrecisionAccuracy RFC 0.4642 0.5302 0.5718 0.5385 0.6275 0.6587 0.7315 0.6620 CRNN 0.5249 0.5305 0.5296 0.5919 0.6367 0.6433 0.6419 0.757 The Twitter15 and Twitter16 datasets - Early Detection of Rumours on Twitter via Stance Transfer Learning Skip to search form Skip to main content > Semantic Scholar's Logo. Search. Sign In Create Free Account. You are currently offline. Some features of the site may not work correctly

This proposed approach will be evaluated based on 2 public Twitter datasets: Twitter15 and Twitter16, which respectively contain 1381 and 1181 propagation trees. In each dataset, a group of widespread source tweets along with their propagation threads (i.e., replies, retweets), are provided in the form of tree structures There is no public dataset for evaluating complementary summaries. cPTK can detect rumors with 72% accuracy for Twitter15 and 69.0% for Twitter16 within 12 hours, which is much earlier than the baselines and the mean official report times (a) Twitter15 (b) Twitter16

BiGCN: Source Codes: Rumor Detection on Social Media with

  1. We evaluate our method on three large-scale datasets, Twitter15/16 and Weibo. Experimental results demonstrate the superiority of the proposed method. The rest part of this paper is organized as follows: Section 2 presents a detailed description of the related works of this paper
  2. Twitter15 1,490 y y y y Twitter Tweets from [Liu et al., 2015; Ma et al.,2016] Twitter16 818 y y y y Twitter Tweets from [Ma et al., 2017b] BuzzFeedNews 2,282 y Facebook Facebook data from [Silverman et al., 2016] SemEval19 325 y y y y Twitter, Reddit SemEval 2019 Task 7 data set. Kaggle Emergen
  3. have achieved 85% accuracy in their Provenance Based Approach. They have used twitter and weibo dataset for their experiment. They also modified and added the contents to current dataset based on their method before sending it to the classification phase
  4. Search Engines: google yahoo bing citeseer baidu KDD Reading in Taiwan Normal University . An Open-Source Data Mining Library Part IV. Classification. DM688.

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Early Detection of Rumours on Twitter via Stance Transfer

To the best of our knowledge, we are the first to evaluate our models on two rumor detection data sets: the PHEME data set as well as the Twitter15 and Twitter16 data sets. We show that our best models outperform current state-of-the-art models for both data sets. Experiments on the Twitter15, Twitter16 and PHEME datasets show that our model is more effective than several state-of-the-art baselines. Keywords: Rumor detection · User and sentiment information · Hierarchical attention networ Rumor-Detection The public code for paper A Graph Convolutional Encoder and Decoder Model for Rumor Detection which is accepted by DSAA 2020. Table of Contents data After decompress data.rar, you can get three folds named Twitter15,Twitter16, Weibo.Each directory contains two types of file: feature file and label file Dataset Best of Others Spp (Ours) Twt13 72.79 72.80 Twt14 73.60 74.42 Twt15 64.84 63.73 LvJn14 74.52 75.34 SMS13 68.37 67.16 Sarc14 59.11 42.86 Table 2: We compare the macro-averaged F1-scores of our system (Spp) with the best results of other teams in SemEval-2015. Our system achieves the highest F1-scores on three out of six datasets

Rumor Detection by Propagation Embedding Based on Graph

MAKE Free Full-Text Rumor Detection Based on SAGNN

We address rumor detection by learning to differentiate between the community's response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model. Datasets. We evaluate the proposed AARD on three public datasets including Pheme (Zubiaga et al.,2016), Twitter15 and Twitter16 (Ma et al., 2017) datasets since these datasets contain source posts, the corresponding responses, and the rumor labels. The original labels of Twitter15 and Twit-ter16 datasets include four classes, i.e., true rumor 1054 D.T.VuandJ.J.Jung./InternationalJournalofComputationalIntelligenceSystems14(1)1053-1065 Figure1 ExampleofarumortweetpropagatingonTwitter We are extracting the textual content of source tweets in form of RoBERTa text's vector representations which is the current state-of-the-art for text embedding. The model is trained on benchmark datasets of Twitter15 and Twitter16 along with scrapped data of users retweeting in their current state

Symmetry Free Full-Text Rumor Detection on Social

Experiments on Sina Weibo, Twitter15, and Twitter16 rumor detection datasets demonstrate the proposed model's superiority over baseline methods. We also conduct an ablation study to understand the relative contributions of the various aspects of the method we proposed Social media had a revolutionary impact because it provides an ideal platform for share information; however, it also leads to the publication and spreading of rumors. Existing rumor detection methods have relied on finding cues from only user-generated content, user profiles, or the structures of wide propagation. However, the previous works have ignored the organic combination of wide. Table 2. List of User Characteristics Extracted from Twitter15 User Profiles - FNE

Learning Automata-based Misinformation Mitigation via

DataSet LM LM + SCDV MB MB + SCDV AP 0.2742 0.2856 0.3283 0.3395 SJM 0.2052 0.2105 0.2341 0.2409 WSJ 0.2618 0.2705 0.3027 0.3126 Robust04 0.2516 0.2684 0.2819 0.2933 Mean average precision (MAP) on Information Retrieval Datasets 2 Document Vector Estimation using Partition Word-Vectors Averaging Vivek Gupta PhD Student School of Computing University of Utah 20 June 2019 IBM Intern Highlight Tal 82 xiii LIST OF FIGURES Continued Figure Page 63 Architecture of the Status from IT 236 at St. John's Universit Mitigating misinformation on social media is an unresolved challenge, particularly because of the complexity of information dissemination. To this end, Multivariate Hawkes Proces

1.Split the dataset into 4 parts, here called flip-train,flipdev1 flipdev2 and fliptest. 2.Train a classifier (e.g. SVM) on the set fliptrain, using the original full set of features. 3.Calculate prediction score on datasets flipdev1 and flipdev2. 4.Pick a subset S of words from the vocabulary of fliptrain. This is the word-pool for. TOOLS 2.0 IN LIBRARY ASSOCIATIONS AND NATIONAL LIBRARIESMLAS Midyear Meeting Report (Feb. 2013) Author: Glòria Pérez-Salmerón, Director of the Biblioteca Nacional de EspañaTechnical Advisory: Mar Pérez Morillo, Chief of the Web Service at the Biblioteca Nacional de Españ

情感识别数据集大全一、公开多模态数据集1、Belfast Database2、MIT-BIH3、Aubt4、Multi-ZOL5、Twitter15 原文: Introduction The PubFig database is a large, real-world face dataset consisting of58,797images of200people collected from the internet Kostas Tzoumas Data Science Summit Europe June 6, 2016. Kostas Tzoumas@kostas_tzoumas. Hadoop Summit San JoseJune 6, 2016. Streaming in the Wild with Apache FlinkT This paper presents a novel approach to recurrent neural network (RNN) regularization. Differently from the widely adopted dropout method, which is applied to forward connections of feed-forward architectures or RNNs, we propose to drop neurons directly in recurrent connections in a way that does not cause loss of long-term memory. Our approach is as easy to implement and apply as the regular. Suppose that n is a copying bound for the input XTOP R M, which means that no more than n rules are applied to each input symbol. The first XTOP R is actually a nondeterministic linear and nondeleting XTOP that annotates each input tree symbol with ex- actly n rules of M that are consistent with the state behavior of M . Moreover, the annotation also pre- scribes with which of n rules the.

Davide Eynard. 1 f2 f Politecnico di Milano Dipartimento di Elettronica e Informazione Dottorato di Ricerca in Ingegneria dell'Informazione A Virtuous Cycle of Semantics and Participation Tesi di dottorato di: Davide Eynard Relatore: Prof. Marco Colombetti Tutore: Prof. Andrea Bonarini Coordinatore del programma di dottorato: Prof. Patrizio. et al.(2017), Twitter15 and Twitter16, are uti-lized. Each dataset contains a collection of source tweets, along with their corresponding sequences of retweet users. We choose only true and fake labels as the ground truth. Since the original data does not contain user profiles, we use user IDs to crawl user information via Twitter API twitter15 782 323 47,324 43 6 458 28 2.2 twitter16 410 191 27,732 46 6 458 29 16.6 Total 2,967 4,655 214,606 rumor source tweets and their contexts associated with six real-world breaking news events. Source tweets are labeled with weak supervision. The augmented dataset expands original one by 200% of source tweets and 100% of social context data

ii Declaration of Authorship I, Lei TONG, declare that this thesis titled, Depression Detection Via Twitter and the work presented in it are my own. I confirm that: This work was done wholly or mainly while in candidature for a researc

Dataset twitter15, twitter16 版权声明:本文为博主原创文章,遵循 CC 4.0 BY-SA 版权协议,转载请附上原文出处链接和本声明 Twitter15 Twitter16 来自 Twitter 15、16 年的帖子,包括了帖子之间的树状收听,关注关系和帖子正文等。 Buzzfeed Election Dataset & Political News Dataset

ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for

【Interpretation of the paper AAAI 2020 | Bi-GCN】Rumor Detection on Social Media with Bi-Directional GCN, Programmer Sought, the best programmer technical posts sharing site 一文看懂虚假新闻检测(附数据集 & 论文推荐). 本人过去几年一直从事内容质量方面的算法工作,近期出于兴趣对假新闻这个问题做了一些调研,简单总结一下提供读者参考。. 在某种程度上假新闻的是一个微观领域问题,它和谣言分类,事实判断,标题党检测. 虚假新闻检测,来自美团NLP团队方案. 2020-03-03 20:05. 来源: 数据分析入门与实战. 原标题:虚假新闻检测,来自美团NLP团队方案. 这篇文章主要以第二名为讨论对象,来自美团NLP团队。. 同时会对比第一名和第三名的方案。. 此外,给出了SemEval2019的答案分类任务上.

作者丨孫子荀 單位丨騰訊科技高階研究員 研究方向丨多模態內容質量 本人過去幾年一直從事內容質量方面的演算法工作,近期出於興趣對假新聞這個問題做了一些調研,簡單總結一下提供讀者參考。在某種程度上假 Gbdt example - megatec.center Gbdt exampl 作者丨孙子荀. 单位丨腾讯科技高级研究员. 研究方向丨多模态内容质量. 本人过去几年一直从事内容质量方面的算法工作,近期出于兴趣对假新闻这个问题做了一些调研,简单总结一下提供读者参考 数据集规模:一共有44305条评论,244569张图片(每条评论的图片有多张),平均每条评论有13个句子,230个单词。. 数据集的情感标注:是对每条评论的情感倾向打1,2,3,4,5五个分值。. 数据内容:从Tumblr收集来的多模态情绪数据集。. Tumblr是一种微博客服务,用户在. Twitter15 Twitter16. 来自 Twitter 15、16 Buzzfeed Election Dataset & Political News Dataset. Buzzfeed's 2016 收集的选举假新闻,以及作者收集的 75. IEMOCAP这个数据集一般下载是需要给原作者发邮箱且需要懂得如何科学上网的,这里我将我下载好的IEMOCAP数据集上传至网盘,方便大家下载!. (压缩后为12.45G,原28G). 做情感数据库这一块的朋友可以下载,资源是一个txt文档,里面有对应的IEMOCAP数据集的网盘.

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