Research Article
An Analysis of Social Data Credibility for Services Systems in Smart Cities – Credibility Assessment and Classification of Tweets
@INPROCEEDINGS{10.1007/978-3-319-67636-4_14, author={Iman Abu Hashish and Gianmario Motta and Tianyi Ma and Kaixu Liu}, title={An Analysis of Social Data Credibility for Services Systems in Smart Cities -- Credibility Assessment and Classification of Tweets}, proceedings={Cloud Infrastructures, Services, and IoT Systems for Smart Cities. Second EAI International Conference, IISSC 2017 and CN4IoT 2017, Brindisi, Italy, April 20--21, 2017, Proceedings}, proceedings_a={IISSC \& CN4IOT}, year={2017}, month={11}, keywords={Smart cities Smart citizens Social data Twitter Twitter bot Credibility Veracity Classification Social media mining Machine learning}, doi={10.1007/978-3-319-67636-4_14} }- Iman Abu Hashish
Gianmario Motta
Tianyi Ma
Kaixu Liu
Year: 2017
An Analysis of Social Data Credibility for Services Systems in Smart Cities – Credibility Assessment and Classification of Tweets
IISSC & CN4IOT
Springer
DOI: 10.1007/978-3-319-67636-4_14
Abstract
In the “Information Age”, Smart Cities rely on a wide range of different data sources. Among them, social networks can play a big role, if information veracity is assessed. Veracity assessment has been, and is, a rather popular research field. Specifically, our work investigates the credibility of data from Twitter, an online social network and a news media, by considering not only credibility, and type, but also origin. Our analysis proceeds in four phases: Features Extraction, Features Analysis, Features Selection, and Classification. Finally, we classify whether a Tweet is credible or incredible, is rumor or spam, is generated by a human or a Bot. We use Social Media Mining and Machine Learning techniques. Our analysis reaches an overall accuracy higher than the benchmark, and it adds the origin dimension to the credibility analysis method.
