DOI:10.18653/v1/S17-2006
Corpus ID: 9164793
SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours
Leon Derczynski, Kalina Bontcheva, +3 authors A. Zubiaga
Published in *SEMEVAL 2017
Computer Science
Media is full of false claims. Even Oxford Dictionaries named “post-truth” as the word of 2016. This makes it more important than ever to build systems that can identify the veracity of a story, and… Expand
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217 Citations
Highly Influential Citations
30
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114
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67
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SemEval
Veracity
Baseline (configuration management)
Wikipedia
Interaction
Population
Kripke semantics
Authentication
Verification and validation
Dictionary
Benchmark (computing)
217 Citations
UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features
Hareesh Bahuleyan, Olga Vechtomova
Computer Science
SemEval@ACL

2017
TLDR
The proposed approach makes use of topic independent features from two categories, namely cue features and message specific features to fit a gradient boosting classifier to identify potential rumours in subtask-A of RumourEval. Expand
24 Citations
PDF
Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM
E. Kochkina, Maria Liakata, Isabelle Augenstein
Computer Science
SemEval@ACL

2017
TLDR
A LSTM-based sequential model is proposed that, through modelling the conversational structure of tweets, achieves an accuracy of 0.784 on the RumourEval test set outperforming all other systems in Subtask A. Expand
68 Citations
PDF
ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models
Feixiang Wang, Man Lan, Yuanbin Wu
Computer Science
SemEval@ACL

2017
TLDR
This paper describes the submissions to task 8 in SemEval 2017, i.e., Determining rumour veracity and support for rumours, and a two-step classifier to address subtask A, which adopted supervised machine learning methods. Expand
26 Citations
PDF
DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics
Ankit Srivastava, Georg Rehm, J. Schneider
Computer Science
SemEval@ACL

2017
TLDR
The Digital Curation Technologies team at the German Research Center for Artificial Intelligence participated in two subtasks and their implementation consisted of a Multivariate Logistic Regression (Maximum Entropy) classifier coupled with hand-written patterns and rules applied in a post-process cascading fashion. Expand
19 Citations
PDF
RumourEval 2019: Determining Rumour Veracity and Support for Rumours
Genevieve Gorrell, Kalina Bontcheva, Leon Derczynski, E. Kochkina, Maria Liakata, A. Zubiaga
Computer Science
NAACL 2019

2018
TLDR
Scope is extended compared with the first RumourEval, in that the dataset is substantially expanded to include Reddit as well as Twitter data, and additional languages are also included.Expand
37 Citations
PDF
CLEARumor at SemEval-2019 Task 7: ConvoLving ELMo Against Rumors
I. Baris Schlicht, Lukas Schmelzeisen, Steffen Staab
Computer Science
*SEMEVAL

2019
TLDR
This paper describes the submission to SemEval-2019 Task 7: RumourEval: Determining Rumor Veracity and Support for Rumors, and provides results and analysis of the system performance and present ablation experiments. Expand
11 Citations
PDF
NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter
Omar Enayet, S. El-Beltagy
Computer Science
SemEval@ACL

2017
TLDR
The results and conclusions of the participation in SemEval-2017 task 8: Determining rumour veracity and support for rumours, which involved participation in 2 subtasks, are presented. Expand
63 Citations
PDF
GWU NLP at SemEval-2019 Task 7: Hybrid Pipeline for Rumour Veracity and Stance Classification on Social Media
S. Hamidian, Mona T. Diab
Computer Science
*SEMEVAL

2019
TLDR
A hybrid system comprising rules and a machine learning model which makes use of replied tweets to identify the veracity of the source tweet and achieved 0.435 F-Macro in stance classification, and 0.801 RMSE in rumor verification tasks in Task7 of SemEval 2019. Expand
3 Citations
PDF
Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules
Marianela Garcia Lozano, Hanna Lilja, Edward Tjörnhammar, Maja Karasalo
Computer Science
SemEval@ACL

2017
TLDR
For the competition SemEval-2017, the possibility of performing stance classification for messages in Twitter conversation threads related to rumours was investigated and the ensemble classification approach of combining convolutional neural networks with both automatic rule mining and manually written rules achieved a final accuracy of 74.9%.Expand
14 Citations
PDF
AndrejJan at SemEval-2019 Task 7: A Fusion Approach for Exploring the Key Factors pertaining to Rumour Analysis
Andrej Janchevski, S. Gievska
Computer Science
*SEMEVAL

2019
TLDR
The current findings strongly demonstrate that supplementary sources of information play significant role in classifying the veracity and the stance of Twitter interactions deemed to be rumourous. Expand
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References
SHOWING 1-10 OF 31 REFERENCES
UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features
Hareesh Bahuleyan, Olga Vechtomova
Computer Science
SemEval@ACL

2017
TLDR
The proposed approach makes use of topic independent features from two categories, namely cue features and message specific features to fit a gradient boosting classifier to identify potential rumours in subtask-A of RumourEval. Expand
24 Citations
PDF
IITP at SemEval-2017 Task 8 : A Supervised Approach for Rumour Evaluation
Vikram Singh, Sunny Narayan, Md. Shad Akhtar, Asif Ekbal, P. Bhattacharyya
Computer Science
SemEval@ACL

2017
TLDR
A supervised classification approach employing several lexical, content and twitter specific features for learning and showing promising results for both the veracity and support for rumours problems is proposed. Expand
19 Citations
PDF
Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM
E. Kochkina, Maria Liakata, Isabelle Augenstein
Computer Science
SemEval@ACL

2017
TLDR
A LSTM-based sequential model is proposed that, through modelling the conversational structure of tweets, achieves an accuracy of 0.784 on the RumourEval test set outperforming all other systems in Subtask A. Expand
68 Citations
PDF
ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models
Feixiang Wang, Man Lan, Yuanbin Wu
Computer Science
SemEval@ACL

2017
TLDR
This paper describes the submissions to task 8 in SemEval 2017, i.e., Determining rumour veracity and support for rumours, and a two-step classifier to address subtask A, which adopted supervised machine learning methods. Expand
26 Citations
PDF
DFKI-DKT at SemEval-2017 Task 8: Rumour Detection and Classification using Cascading Heuristics
Ankit Srivastava, Georg Rehm, J. Schneider
Computer Science
SemEval@ACL

2017
TLDR
The Digital Curation Technologies team at the German Research Center for Artificial Intelligence participated in two subtasks and their implementation consisted of a Multivariate Logistic Regression (Maximum Entropy) classifier coupled with hand-written patterns and rules applied in a post-process cascading fashion. Expand
19 Citations
PDF
IKM at SemEval-2017 Task 8: Convolutional Neural Networks for stance detection and rumor verification
Yi-Chin Chen, Zhao-Yang Liu, Hung-Yu Kao
Computer Science
*SEMEVAL

2017
TLDR
This paper uses a convolutional neural network for short text categorization using multiple filter sizes and beats the baseline classifiers on different event data with good F1 scores. Expand
58 Citations
PDF
NileTMRG at SemEval-2017 Task 8: Determining Rumour and Veracity Support for Rumours on Twitter
Omar Enayet, S. El-Beltagy
Computer Science
SemEval@ACL

2017
TLDR
The results and conclusions of the participation in SemEval-2017 task 8: Determining rumour veracity and support for rumours, which involved participation in 2 subtasks, are presented. Expand
63 Citations
PDF
Mama Edha at SemEval-2017 Task 8: Stance Classification with CNN and Rules
Marianela Garcia Lozano, Hanna Lilja, Edward Tjörnhammar, Maja Karasalo
Computer Science
SemEval@ACL

2017
TLDR
For the competition SemEval-2017, the possibility of performing stance classification for messages in Twitter conversation threads related to rumours was investigated and the ensemble classification approach of combining convolutional neural networks with both automatic rule mining and manually written rules achieved a final accuracy of 74.9%.Expand
14 Citations
PDF
Crowdsourcing the Annotation of Rumourous Conversations in Social Media
A. Zubiaga, Maria Liakata, R. Procter, Kalina Bontcheva, P. Tolmie
Computer Science
WWW

2015
TLDR
A new annotation scheme for capturing rumour-bearing conversational threads, as well as the crowdsourcing methodology used to create high quality, human annotated datasets of rumourous conversations from social media are presented.Expand
56 Citations
PDF
Rumor has it: Identifying Misinformation in Microblogs
Vahed Qazvinian, Emily Rosengren, Dragomir R. Radev, Qiaozhu Mei
Computer Science
EMNLP

2011
TLDR
This paper addresses the problem of rumor detection in microblogs and explores the effectiveness of 3 categories of features: content- based, network-based, and microblog-specific memes for correctly identifying rumors, and believes that its dataset is the first large-scale dataset on rumor detection.Expand
639 Citations
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