Controversy Analysis and Detection

Seeking information on a controversial topic is often a complex task. Alerting users about controversial search results can encourage critical literacy, promote healthy civic discourse and counteract the filter bubble effect, and therefore would be a useful feature in a search engine or browser extension. Additionally, presenting information to the user about the different stances or sides of the debate can help her navigate the landscape of search results beyond a simple list of 10 links . This thesis has made strides in the emerging niche of controversy detection and analysis. The body of work in this thesis revolves around two themes: computational models of controversy, and controversies occurring in neighborhoods of topics. Our broad contributions are: (1) Presenting a theoretical framework for modeling controversy as contention among populations; (2) Constructing the first automated approach to detecting controversy on the web, using a KNN classifier that maps from the web to similar Wikipedia articles; and (3) Proposing a novel controversy detection in Wikipedia by employing a stacked model using a combination of link structure and similarity. We conclude this work by discussing the challenging technical, societal and ethical implications of this emerging research area and proposing avenues for future work

Fake news and coronavirus: Detecting key players and trends through analysis of Twitter conversations

The global health crisis arising from the expansion of Covid-19 has led the WHO to coin the term infodemics to define a situation of fear and insecurity in which the dissemination of false information has become widespread. These hoaxes take advantage of this type of emotion to spread faster than the coronavirus itself, generating fear and distrust in the population. The spread of these lies, part of which circulates on social networks, is dangerous because it affects health and can make the contagion worse and cause people to die. This research aims to analyse and visualise the network created around the false news circulating on Twitter about the coronavirus pandemic using the technique of social network analysis. NodeXL Pro software has been used. Several measures of network centrality have been used to generate the network of connections between users, to represent their interaction patterns and to identify the key actors within the network. In addition, a semantic network has also been created to discover the differences in the way groups of people talk about the topic. The results show that the situation in the USA dominates the conversation, despite the fact that at that time there were hardly any cases, and Europe had become the global epicentre of the Covid-19. Despite reports of inaction by journalists and critics of the Trump government, there are several weeks in which disinformation distracts from taking more effective action and actually preventing contagion. Moreover, among the actors with the most prominent positions in the network, there is little presence of scientists and institutions that help to disprove the hoaxes and explain the hygiene measures.