Identifying propaganda from online social networks during COVID-19 using machine learning techniques
Open Access
- 29 October 2020
- journal article
- research article
- Published by Springer Nature in International Journal of Information Technology
- Vol. 13 (1) , 115-122
Abstract
COVID-19, affected the entire world because of its non-availability of vaccine. Due to social distancing online social networks are massively used in pandemic times. Information is being shared enormously without knowing the authenticity of the source. Propaganda is one of the type of information that is shared deliberately for gaining political and religious influence. It is the systematic and deliberate way of shaping opinion and influencing thoughts of a person for achieving the desired intention of a propagandist. Various propagandistic messages are being shared during COVID-19 about the deadly virus. We extracted data from twitter using its application program interface (API), Annotation is being performed manually. Hybrid feature engineering is performed for choosing the most relevant features.The binary classification of tweets is being performed with the help of machine learning algorithms. Decision tree gives better results among all other algorithms. For better results feature engineering may be improved and deep learning can be used for classification task.Keywords
This publication has 13 references indexed in Scilit:
- Machine learning based approaches for detecting COVID-19 using clinical text dataInternational Journal of Information Technology, 2020
- Different Faces of FalseJournal of Data and Information Quality, 2019
- Diagnosis of diabetes type-II using hybrid machine learning based ensemble modelInternational Journal of Information Technology, 2018
- Text classification algorithms for mining unstructured data: a SWOT analysisInternational Journal of Information Technology, 2018
- Early detection of promoted campaigns on social mediaEPJ Data Science, 2017
- A psychometric analysis of information propagation in online social networks using latent trait theoryComputing, 2015
- $1.00 per RT #BostonMarathon #PrayForBoston: Analyzing fake content on Twitter2013
- Social media? Get serious! Understanding the functional building blocks of social mediaBusiness Horizons, 2011
- The Semantic Web2006
- Cognitive hacking: a battle for the mindComputer, 2002