  The verification of multimedia content over social media is one of the
challenging and crucial issues in the current scenario and gaining prominence
in an age where user-generated content and online social web platforms are the
leading sources in shaping and propagating news stories. As these sources allow
users to share their opinions without restriction, opportunistic users often
post misleading/ unreliable content on social media such as Twitter, Facebook,
etc. At present, to lure users towards the news story, the text is often
attached with some multimedia content (images/videos/audios). Verifying these
contents to maintain the credibility and reliability of social media
information is of paramount importance. Motivated by this, we proposed a
generalized system that supports the automatic classification of images into
credible or misleading. In this paper, we investigated machine learning-based
as well as deep learning-based approaches utilized to verify misleading
multimedia content, where the available image traces are used to identify the
credibility of the content. The experiment is performed on the real-world
dataset (Media-eval-2015 dataset) collected from Twitter. It also demonstrates
the efficiency of our proposed approach and features using both Machine and
Deep Learning Model (Bi-directional LSTM). The experiment result reveals that
the Microsoft bings image search engine is quite effective in retrieving titles
and performs better than our study's Google image search engine. It also shows
that gathering clues from attached multimedia content (image) is more effective
than detecting only posted content-based features.
