Affiliations:
Department of Data Science, Riphah International University, Islamabad, Pakistan
Correspondence:
[*]
Corresponding author. Muhammad Abu Talha, Department of Data Science, Riphah International University, Islamabad, Pakistan. Tel.: +923216021602; E-mail: talharamzan.tr@gmail.com.
Abstract: False information is becoming more frequent in distributing disinformation by distorting people’s awareness and decision-making by altering their views or knowledge. The propagation of disinformation has been aided by the proliferation of social media and online forums. Allowing it to readily blend in with true information. Parody news and rumors are the most common types of misleading and unverified information, and they should be caught as soon as possible to avoid their disastrous consequences. As a result, in recent years, there has been a surge in interest in effective detection approaches. For this study, a customized dataset was built that included both real and parody tweets from Pakistan and India. This study proposes a two-step strategy for detecting parody tweets. In the first stage of the approach the unstructured data is converted into structured data set. In the second step, multiple supervised artificial intelligence algorithms were employed. An experimental assessment of the different classification methods inside a customized dataset was undertaken in this study, and these classification models were compared using evaluation metrics. Our results showed accuracy of 92%.
Keywords: Social media, parody tweets, binary classification, machine learning, deep learning, word embedding
