Countering Rumours in Online Social Media

The phenomenon of rumour spreading refers to a collective process where people participate in the transmission of unverified and relevant information to make sense of the ambiguous, dangerous, or threatening situation. The dissemination of rumours on a large scale no matter with what purpose could precipitate catastrophic repercussions. This research aims at addressing this challenge systematically. More in detail, the primary research objective of this dissertation is

To systematically study the rumour confrontation within online social media.

To accomplish this objective, six steps are taken. At first, the conceptualisation of the main construct in this research is investigated. There are myriad of concepts in English language implying false or unverified information. However, despite years of academic research, there is no consensus regarding their conceptualisation, and they are often used interchangeably or conflated into one idea. This problem could become an obstacle to countering the surge of false information by creating confusion, distracting the community’s attention, and draining their efforts. In the first step, this dissertation addresses this challenge by providing a process-based reading of false and unverified information. This view argues that although the genesis of such information might be deliberate or inadvertent and with different purposes, they primarily disseminate on the basis of similar motives and follow the same process.
After settling the conceptualisation problem, the next step investigates the role of communication mediums and especially online social media in the spread of rumours. Although the phenomenon of rumour dissemination has drawn much attention over the past few years, it is an ancient phenomenon. The rumours used to circulate through primitive forms of communications such as word of mouth or letters; however, the technological development, particularly social media, escalated the scale, speed, and scope of this phenomenon. This step aims to pinpoint the features privy to social media that facilitate the emergence and the spread of rumours. Especially, an exclusive automation mechanism of recommendation systems in social media is closely examined through a set of experiments based on YouTube data.
The third step in this study investigates the constellation of past counter-rumour strategies. Although rumour spreading and its potentially destructive effects have been taken into account since ancient times, it was only less than a century ago that the first systematic efforts against the mass spread of rumours began. Since then, a series of strategies have been practised by various entities; nevertheless, the massive waves of rumours are still sweeping over individuals, organisations, and societal institutions. In order to develop an effective and comprehensive plan to quell rumours, it is crucial to be aware of the past counter strategies and their potential capabilities, shortcomings and flaws. In this step, we collect the counter strategies over the past century and set them in the epidemic control framework. This framework helps to analyse the purpose of the strategies which could be (i) exposure minimisation, (ii) immunisation or vaccination, and (iii) reducing the transmission rate. The result of the analysis allows us to understand, what aspects of confrontation with rumour have been targeted extensively and what aspects are highly neglected.


Following the discussion on the epidemic framework, one of the most effective approaches to rumour confrontation is the immunisation which is primarily driven by academia. The fourth step investigates the readiness of academia in this subject domain. When we do not know the readiness level in a particular subject, we either overestimate or underestimate our ability in that subject. Both of these misjudgements are incorrect and lead to decisions irrelevant to the existing circumstance. To tackle this challenge, the technology emergence framework is deployed to measure academia's readiness level in the topic of rumour circulation. In this framework, we study four dimensions of emergence (novelty, growth, coherence and impact) over more than 21,000 scientific articles, to see the level of readiness in each dimension. The results show an organic growth which is not sufficiently promising due to the surge of rumours in social media. This challenge could be tackled by creating exclusive venues that lead to the formation of a stable community and realisation of an active field for rumour studies.
The other aspect of the epidemic framework involves exposure minimisation and transmission rate reduction, which are addressed in the fifth step by an artificial intelligence based solution. The drastic increase in the volume, velocity, and the variety of rumours entails automated solutions for the inspection of circulating contents in social media. In this vein, binary classification is a dominant computational approach; however, it suffers from non-rumour pitfall, which makes the classifier unreliable and inconsistent. To address this issue a novel classification approach is utilised which only uses one rather than multiple classes for the training phase. The experimentation of this approach on two major datasets shows a promising classifier that can recognise rumours with a high level of F1-score.
The last step of this manuscript approaches the topic of rumour confrontation from a pro-active perspective. The epidemic framework helps to develop solutions to control rumour dissemination; however, they mostly adopt a passive approach which is reactive and after-the-fact. This step introduces an ontology model that can capture the underlying mechanisms of social manipulation operations. This model takes a proactive stance against social manipulation and provides us with an opportunity of developing preemptive measures. The model is evaluated by the experts and through exemplification on three notoriously famous social manipulation campaigns.