17 January. 2021
The COVID-19 pandemic has underscored the need for tools and methodologies for combating online disinformation in ways that empower people, at the same protecting democratic values. While the detection of “fake news” is far from being a new topic, the urgency and the universality of this unprecedented situation has resulted in renewed attention to the spread of disinformation on the Web and in social media. According to the World Health Organization (WHO), the pandemic has brought about an “infodemic,” in which people have become overexposed to information about the problem, with much of the information available being of dubious credibility.
Recent research has revealed the significant role that visual artefacts - such as images and memes - play in the spread of misinformation and disinformation concerning COVID-19. In short, visuals are important in the context of science communication (e.g., messages concerning COVID-19) because they serve as indices, having a physical connection to their referent; thus, in contrast to symbols or icons, they are often viewed as being closer to the truth as compared to other forms of communication. Furthermore, although in recent years, there is a proliferation of research surrounding both the automated methods for the detection of misinformation as well as on media literacy for a range of stakeholders (from the role of teachers to professional journalists), few approaches focus specifically on visual information.
The current project aims to create a high-quality and valid dataset of COVID-related images (such as infographics, news covers, etc.). The dataset will be annotated using a valid crowdsourcing methodology which will be discussed between the successful applicant and the TAG MRG team. The methodology might consist of data collection from the web or selected social media, while also will include the use of crowdworker platforms such as Amazon MTurk or Appen, or the in-house award-winning system of the team, OpenTag. Another aim is to validate the developed dataset with public fact-checking techniques or tools to ensure the quality of the final product. Furthermore, basic research, data collection and analysis will be conducted to provide insights about the developed dataset.
The intern must be a junior or senior university student (3rd year of studies or more) in Computer Science or related fields of study.
The successful applicant must have basic knowledge in:
Python or R programming language for data collection & analysis
Using Amazon MTurk, Appen or other crowdsourcing platform will be considered a plus
Any prior knowledge or experience on the Risk Communication Theory or Fake News/Disinformation or Crowdsourcing via Crowdworkers will be considered as a plus.
The project aims to design and execute several crowdsourcing studies using Amazon MTurk or Appen crowdworker platforms to annotate the gathered images for disinformation checking and answer to specific research questions. The successful applicant may develop Python/R tools for analyzing the collected data and conclude into fundings, while he/she will be able to use some of the groups’ prior research tools (eg. OpenTag platform). The project will involve work in data collection and analysis.
The successful applicant will discuss with the TAG MRG team about the final expected deliverables. Although some main expected deliverables would be the following:
The team will ensure that the successful applicant will have the opportunity to get involved in a high-quality research environment and expert team, while also aiming to generate a paper for submission in a competitive Conference, Workshop, or Journal on the related topic. The publication will include the successful applicant’s (intern) name as one of the authors of the current work.