|Description||TraceMap approaches fake news from a completely different angle. Instead of looking at content, we expose diffusion patterns of social media posts. To make news distribution transparent, we are developing an interactive webtool that will allow everyone to explore how posts spread within a social network.|
|Persons working on|
|Tags||Fake News, Data Visualization, Social Media, Populism, Data Science, Neo4j, Twitter|
Social media are amongst the most used online spaces for news consumption. The problem of fake news diffusion in social media has gained a great deal of attention in the last years, and specialists try to combat it worldwide. Most of the attempts focus on content analysis, a process that often has to be done manually. TraceMap proposes an innovative approach to tackle this problem: instead of analysing content, we will provide a visualization tool and collective debunking platform. We are developing an interactive webtool that allows users to visualize the patterns of news diffusion within a social network. Our aim is to empower social media users to actively engage in debunking, making it more difficult for fake news to propagate further.
To accomplish that, we will gather friendship relation data from social networks (Twitter, initially) and build a graph database – a cutting-edge technology well suited for this kind of data. Our open-source algorithms reconstruct the path each post might have taken while spreading from its origin. With a URL of a post, any person can generate in a few seconds its “tracemap”, a diagram composed of nodes and edges. Nodes represent user profiles that shared that post; edges depict how these users are related to each other. Through TraceMap’s interactive features, it is possible to identify particularly influential users, eco-chambers and to obtain additional (meta)information about the post in question – for example, how fast and how far that post has gone. We want to offer this open-source platform to all social media users. TraceMap exclusively uses public profile data, and erases users from the database as soon as they change their privacy settings on the corresponding social medium.
The mechanisms of news diffusion on social networks is not transparent to the general user. Social media’s filtering algorithms play an important role: these platforms analyse users’ activities to estimate the impact of posts on their newsfeeds. Such algorithms (the so-called “filter bubbles”) decide which content should be displayed to each individual. Therefore, posts in a user’s newsfeed do not necessarily reflect any journalistic relevance. This becomes a problem when people only consume news inside social media: their worldview gets extremely biased. Facing these challenges, the TraceMap project develops several technical answers, aiming at four main goals:
1. Enhance users’ awareness, by exposing the mechanisms of news diffusion, the role of each user and the consequences of one’s online activities.
2. Empower social media users to identify fake news and to distinguish real news from fake news.
3. Establish a platform for collective fact checking, to weaken fake news.
4. Analysis of Diffusion Patterns: an automated evaluation of the trustworthiness of a post.