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Gaussian Processes for Rumour Stance Classification in Social Media

Michal Lukasik, Kalina Bontcheva, Trevor Cohn, Arkaitz Zubiaga, Maria Liakata, Rob Procter
2019 ACM Transactions on Information Systems  
Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ultimately enabling the flagging of highly disputed rumours as  ...  Social media tend to be rife with rumours while new reports are released piecemeal during breaking news.  ...  Gaussian Processes for Classification.  ... 
doi:10.1145/3295823 fatcat:iqmedd32ejdopmp5efu5wrvnnu

Using Gaussian Processes for Rumour Stance Classification in Social Media [article]

Michal Lukasik, Kalina Bontcheva, Trevor Cohn, Arkaitz Zubiaga, Maria Liakata, Rob Procter
2016 arXiv   pre-print
Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ultimately enabling the flagging of highly disputed rumours as  ...  Social media tend to be rife with rumours while new reports are released piecemeal during breaking news.  ...  Gaussian Processes for Classification.  ... 
arXiv:1609.01962v1 fatcat:uzik6vmcdfcavmzzns5xikvobq

Hawkes Process Classification through Discriminative Modeling of Text [article]

Rohan Tondulkar, Manisha Dubey, P.K. Srijith, Michal Lukasik
2020 arXiv   pre-print
We demonstrate the advantages of the proposed techniques on standard benchmarks for rumour stance classification.  ...  Moreover, high complexity and dynamics of the posts in social media makes text classification a challenging problem.  ...  RELATED WORK 2.1 Rumour Stance Classification Stance classification problems in social media were tried to solve using tree based structures like Linear-Chain conditional random field (CRF) and Tree CRF  ... 
arXiv:2010.11851v1 fatcat:psj5awugyrgpxhkd2smzxerzye

Discourse-aware rumour stance classification in social media using sequential classifiers

Arkaitz Zubiaga, Elena Kochkina, Maria Liakata, Rob Procter, Michal Lukasik, Kalina Bontcheva, Trevor Cohn, Isabelle Augenstein
2018 Information Processing & Management  
Rumour stance classification, defined as classifying the stance of specific social media posts into one of supporting, denying, querying or commenting on an earlier post, is becoming of increasing interest  ...  We show that sequential classifiers that exploit the use of discourse properties in social media conversations while using only local features, outperform non-sequential classifiers.  ...  observed around rumours in social media.  ... 
doi:10.1016/j.ipm.2017.11.009 fatcat:z7hea76bzvajdhvnlurxlxrrni

Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification [article]

Maunika Tamire, Srinivas Anumasa, P.K. Srijith
2021 arXiv   pre-print
Our experiments demonstrate that RNODE and Bi-RNODE are effective for the problem of stance classification of rumours in social media.  ...  Classification of posts in social media such as Twitter is difficult due to the noisy and short nature of texts.  ...  Gaussian Processes for Rumour Stance Classification in Social Media. ACM Trans. Inf. Syst. 37, 2 (2019).  ... 
arXiv:2112.12809v1 fatcat:aqcho23wgndprfmxanb66z5wqa

Hawkes Processes for Continuous Time Sequence Classification: an Application to Rumour Stance Classification in Twitter

Michal Lukasik, P. K. Srijith, Duy Vu, Kalina Bontcheva, Arkaitz Zubiaga, Trevor Cohn
2016 Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)  
Classification of temporal textual data sequences is a common task in various domains such as social media and the Web.  ...  In this paper we propose to use Hawkes Processes for classifying sequences of temporal textual data, which exploit both temporal and textual information.  ...  In this paper, we propose to use Hawkes Processes (Hawkes, 1971) , commonly used for modelling information diffusion in social media (Yang and Zha, 2013; De et al., 2015) , for the task of rumour stance  ... 
doi:10.18653/v1/p16-2064 dblp:conf/acl/LukasikSVBZC16 fatcat:t7r4qp4znfetlikj72z3ejwufi

Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations [article]

Arkaitz Zubiaga, Elena Kochkina, Maria Liakata, Rob Procter, Michal Lukasik
2016 arXiv   pre-print
Rumour stance classification, the task that determines if each tweet in a collection discussing a rumour is supporting, denying, questioning or simply commenting on the rumour, has been attracting substantial  ...  Previous work addressing the stance classification task has treated each tweet as a separate unit.  ...  Moreover, our approach takes into account the interaction between users on social media, whether it is about appealing for more information in order to corroborate a rumourous post (querying) or to say  ... 
arXiv:1609.09028v2 fatcat:khtcpvc4xbd2lp3dpsyxv7m4ze

Detection and Resolution of Rumours in Social Media

Arkaitz Zubiaga, Ahmet Aker, Kalina Bontcheva, Maria Liakata, Rob Procter
2018 ACM Computing Surveys  
stance classification and rumour veracity classification.  ...  We summarise the efforts and achievements so far towards the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for detection  ...  Lukasik et al. (2016) investigated Gaussian processes as rumour stance classiiers. For the irst time, the authors also used Brown clusters to extract the features for each tweet.  ... 
doi:10.1145/3161603 fatcat:fl2nsxykofb65iadwizk5sbkru

Simple Open Stance Classification for Rumour Analysis [article]

Ahmet Aker, Leon Derczynski, Kalina Bontcheva
2017 arXiv   pre-print
This paper describes a surprisingly simple and efficient classification approach to open stance classification in Twitter, for rumour and veracity classification.  ...  Stance classification determines the attitude, or stance, in a (typically short) text.  ...  Lukasik et al. (2016) investigate Gaussian Processes as rumour stance classifier. For the first time the authors also use Brown Clusters to extract the features for each tweet.  ... 
arXiv:1708.05286v2 fatcat:yxtnvuyfybeujfev7eio5rtdze

Simple Open Stance Classification for Rumour Analysis

Ahmet Aker, Leon Derczynski, Kalina Bontcheva
2017 RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning  
Lukasik et al. (2016) investigate Gaussian Processes as rumour stance classifier. For the first time the authors also use Brown Clusters to extract the features for each tweet.  ...  Open stance classification is often applied in rumour resolution.  ... 
doi:10.26615/978-954-452-049-6_005 dblp:conf/ranlp/AkerDB17 fatcat:jxhzfxl3y5g4riu227qqnnj4oq

Rumour Detection Models & Tools for Social Networking Sites

2019 International Journal of Engineering and Advanced Technology  
This survey compares the various RDM strategies and Tools that were proposed earlier for identifying the rumour words in social media platforms.  ...  Subsequently, mischievous people started sharing of rumours via social networking sites for gaining personal benefits.  ...  for Rumour Classification by rumour tracking, stance & Veracity classification Applicability to other domains need to be carried out, such as hoaxes and fake news. 8.  ... 
doi:10.35940/ijeat.b3465.129219 fatcat:oyapwqs4cnhtffk3bmxbw7wmxe

Combating Fake News with Transformers: A Comparative Analysis of Stance Detection and Subjectivity Analysis

Panagiotis Kasnesis, Lazaros Toumanidis, Charalampos Z. Patrikakis
2021 Information  
Many natural language processing methods have been proposed in the past to assess a post's content with respect to its reliability; however, end-to-end approaches are not comparable in ability to human  ...  To overcome this, in this paper, we propose the use of a more modular approach that produces indicators about a post's subjectivity and the stance provided by the replies it has received to date, letting  ...  Acknowledgments: The work presented in this paper was supported through the European Commission's H2020 Innovation Action programme under project EUNOMIA (grant agreement no. 825171).  ... 
doi:10.3390/info12100409 fatcat:7zec2eshpbd2rkvwwpvj5hhb3m

A hybrid model for fake news detection: Leveraging news content and user comments in fake news

Marwan Albahar
2021 IET Information Security  
Nowadays, social media platforms such as Twitter have become a popular medium for people to spread and consume news because of their easy access and the rapid proliferation of news.  ...  Detecting such news on social media platforms has become a challenging task. One of the main challenges is identifying useful information that is exploited as a way to detect fake news.  ...  The embedding process was carried out using bidirectional gated recurrent units (GRUs) and a support vector machine (SVM) with a Gaussian kernel for classification.  ... 
doi:10.1049/ise2.12021 fatcat:kz2oi5dkqzfnjcdfozhsfse4si

Towards Explainable Fact Checking [article]

Isabelle Augenstein
2021 arXiv   pre-print
Its contributions go beyond fact checking, with the thesis proposing more general machine learning solutions for natural language processing in the area of learning with limited labelled data.  ...  These automatic methods are often content-based, using natural language processing methods, which in turn utilise deep neural networks to learn higher-order features from text in order to make predictions  ...  “Discourse-aware rumour stance classification in social media using sequential classifiers”.  ... 
arXiv:2108.10274v2 fatcat:5s4an6irezcjfmvvhmiaeqarh4

Fake News Detection in Arabic Tweets during the COVID-19 Pandemic

Ahmed Redha Mahlous, Ali Al-Laith
2021 International Journal of Advanced Computer Science and Applications  
We can conclude that performing further pre-processing did not enhance the classification results with the text from social media.  ...  More than 270K tweets were collected, containing 89 and 88 rumour and non-rumour events, respectively. A supervised Gaussian Naïve Bayes classification algorithm reported an F1-score of 78.6%.  ... 
doi:10.14569/ijacsa.2021.0120691 fatcat:fczyw6d725ggdlu7thijhlald4
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