Publication Type
Journal Article
Version
acceptedVersion
Publication Date
12-2018
Abstract
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.
Keywords
Rumor detection, Computational trust, Representation learning, Recurrent neural networks
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Social Network Analysis and Mining
Volume
8
Issue
64
First Page
1
Last Page
16
ISSN
1869-5450
Identifier
10.1007/s13278-018-0540-z
Publisher
Springer Verlag (Germany)
Citation
RATH, Bhavtosh; GAO, Wei; MA, Jing; and SRIVASTAVA, Jaideep.
Utilizing computational trust to identify rumor spreaders on Twitter. (2018). Social Network Analysis and Mining. 8, (64), 1-16.
Available at: https://ink.library.smu.edu.sg/sis_research/4546
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s13278-018-0540-z