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Part 5: Social Media and Open Computing; International audience; Bright ICT promises a new era of IT adoption and use, however, in this current era of ubiquitous computing and social media platforms, we have witnessed IS users being rendered powerless in the information systems development process and professionally manipulated by large technology companies through the algorithmic structures of social networking platforms. In 1987 Markus & Bjorn-Andersen warned us of the potential of consequences of this situation which are now evident on a daily basis where data privacy is compromised, social media platform content is difficult if not impossible to manage and our political and economic systems are disrupted. This paper outlines an engaged research approach being taken by the RISE_SMA project to develop more innovative theoretical approaches and methods for analysing social media data for the assurance of social cohesion during times of crisis.

Part 4: Security, Privacy, Ethics and Misinformation; International audience; Twitter has become a fertile ground for rumours as information can propagate to too many people in very short time. Rumours can create panic in public and hence timely detection and blocking of rumour information is urgently required. We proposed and compare machine learning classifiers with a deep learning model using Recurrent Neural Networks for classification of tweets into rumour and non-rumour classes. A total thirteen features based on tweet text and user characteristics were given as input to machine learning classifiers. Deep learning model was trained and tested with textual features and five user characteristic features. The findings indicate that our models perform much better than machine learning based models.