Misinformation on social media has significant, and sometimes dramatic, consequences, causing harm to individuals, communities, and even entire societies. We have seen how the dissemination of disinformation on social media affects everything from vaccines or the treatment of diseases to the spread of false information in an attempt to destabilize our governments or influence our votes. The rapid spread of misinformation on social media can make it difficult for people to distinguish fact from fiction and can undermine trust in reliable sources of information.
The DSAA panel will discuss how computational methods such as natural language processing, machine learning, and network analysis can be used to detect and address misinformation on social media. These methods can help identify patterns and trends in the spread of misinformation and can be used to develop targeted interventions to
counter it. For example, AI algorithms can be used to analyze the language and content of social media posts to detect false or misleading information, while network analysis can help identify the sources of misinformation and the individuals or groups who are most vulnerable to its effects. By leveraging these computational methods,
we can better understand the dangers of misinformation on social media and develop effective strategies to mitigate its impact.
Charalampos Tsourakakis (Boston University)
- Ramon Salaverria Aliaga (University of Navarra),
- David Camacho (Polytechni University of Madrid),
- Ioannis Kompatsiaris (ITI/CERTH)
- Paolo Rosso (Polytechnic University of Valencia)
