Utilizing computational trust to identify rumor spreaders on Twitter

Ubiquitous use of social media such as microblogging platforms opens unprecedented chances for false information to diffuse online. Facing the challenges in such a so-called “post-fact” era, it is very important for intelligent systems to not only check the veracity of information but also verify th...

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Main Authors: RATH, Bhavtosh, GAO, Wei, MA, Jing, SRIVASTAVA, Jaideep
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Language:English
Published: Institutional Knowledge at Singapore Management University 2018
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Online Access:https://ink.library.smu.edu.sg/sis_research/4546
https://ink.library.smu.edu.sg/context/sis_research/article/5549/viewcontent/Rath2018_Article_UtilizingComputationalTrustToI.pdf
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spelling sg-smu-ink.sis_research-55492019-12-26T09:05:41Z Utilizing computational trust to identify rumor spreaders on Twitter RATH, Bhavtosh GAO, Wei MA, Jing SRIVASTAVA, Jaideep Ubiquitous use of social media such as microblogging platforms opens unprecedented chances for false information to diffuse online. Facing the challenges in such a so-called “post-fact” era, it is very important for intelligent systems to not only check the veracity of information but also verify the authenticity of the users who spread the information, especially in time-critical situations such as real-world emergencies, where urgent measures have to be taken for stopping the spread of fake information. In this work, we propose a novel machine-learning-based approach for automatic identification of the users who spread rumorous information on Twitter by leveraging computational trust measures, in particular the concept of Believability. We define believability as a measure for assessing the extent to which the propagated information is likely being perceived as truthful or not based on the proxies of trust such as user’s retweet and reply behaviors in the network. We hypothesize that the believability between two users is proportional to the trustingness of the retweeter/replier and the trustworthiness of the tweeter, which are complementary to one another for representing user trust and can be inferred from trust proxies using a variant of HITS algorithm. With the trust network edge-weighted by believability scores, we apply network representation learning algorithms to generate user embeddings, which are then used to classify users into rumor spreaders or not based on recurrent neural networks (RNN). Experimented on a large real-world rumor dataset collected from Twitter, it is demonstrated that our proposed RNN-based method can effectively identify rumor spreaders and outperform four more straightforward, non-RNN models with large margin. 2018-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4546 info:doi/10.1007/s13278-018-0540-z https://ink.library.smu.edu.sg/context/sis_research/article/5549/viewcontent/Rath2018_Article_UtilizingComputationalTrustToI.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Rumor detection Computational trust Representation learning Recurrent neural networks Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Rumor detection
Computational trust
Representation learning
Recurrent neural networks
Databases and Information Systems
spellingShingle Rumor detection
Computational trust
Representation learning
Recurrent neural networks
Databases and Information Systems
RATH, Bhavtosh
GAO, Wei
MA, Jing
SRIVASTAVA, Jaideep
Utilizing computational trust to identify rumor spreaders on Twitter
description Ubiquitous use of social media such as microblogging platforms opens unprecedented chances for false information to diffuse online. Facing the challenges in such a so-called “post-fact” era, it is very important for intelligent systems to not only check the veracity of information but also verify the authenticity of the users who spread the information, especially in time-critical situations such as real-world emergencies, where urgent measures have to be taken for stopping the spread of fake information. In this work, we propose a novel machine-learning-based approach for automatic identification of the users who spread rumorous information on Twitter by leveraging computational trust measures, in particular the concept of Believability. We define believability as a measure for assessing the extent to which the propagated information is likely being perceived as truthful or not based on the proxies of trust such as user’s retweet and reply behaviors in the network. We hypothesize that the believability between two users is proportional to the trustingness of the retweeter/replier and the trustworthiness of the tweeter, which are complementary to one another for representing user trust and can be inferred from trust proxies using a variant of HITS algorithm. With the trust network edge-weighted by believability scores, we apply network representation learning algorithms to generate user embeddings, which are then used to classify users into rumor spreaders or not based on recurrent neural networks (RNN). Experimented on a large real-world rumor dataset collected from Twitter, it is demonstrated that our proposed RNN-based method can effectively identify rumor spreaders and outperform four more straightforward, non-RNN models with large margin.
format text
author RATH, Bhavtosh
GAO, Wei
MA, Jing
SRIVASTAVA, Jaideep
author_facet RATH, Bhavtosh
GAO, Wei
MA, Jing
SRIVASTAVA, Jaideep
author_sort RATH, Bhavtosh
title Utilizing computational trust to identify rumor spreaders on Twitter
title_short Utilizing computational trust to identify rumor spreaders on Twitter
title_full Utilizing computational trust to identify rumor spreaders on Twitter
title_fullStr Utilizing computational trust to identify rumor spreaders on Twitter
title_full_unstemmed Utilizing computational trust to identify rumor spreaders on Twitter
title_sort utilizing computational trust to identify rumor spreaders on twitter
publisher Institutional Knowledge at Singapore Management University
publishDate 2018
url https://ink.library.smu.edu.sg/sis_research/4546
https://ink.library.smu.edu.sg/context/sis_research/article/5549/viewcontent/Rath2018_Article_UtilizingComputationalTrustToI.pdf
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