# MeV NetworkX: Network Analysis and Visualization for Tracing Disinformation
## 1 Introduction  
The increasing digitalization of our world offers significant opportunities for groundbreaking investigative journalism, new models of cross-border collaborative reporting, and access to treasure troves of knowledge and diverse sources at a mouse-click [1]. However, journalists struggle every day to cope with the overwhelming amount of information that emerges online. This combined with a time pressure to verify information as quickly as possible has caused a need for tools that can provide automatic or semi-automatic assistance to arise. Social Network Analysis (SNA) is a field that researchers turn to in order to build tools that can assist journalists in investigating topics disseminated through social media platforms by observing the propagation of claims and rumors, the discussions around the claims and rumors, and interactions between users. Coupled with intelligent methods that extract and process metadata, these tools can provide disinformation-related cues to journalists and fact-checkers and become vital in the daily activities of these professionals. Networks are complex systems of actors, referred to as nodes, interconnected via relationships called edges. On social media, a node can be an account (i.e., a user, page, or group), a URL (i.e., an article or media item), or a keyword (i.e., a hashtag). When two nodes interact (i.e., when a Twitter account retweets a tweet of another Twitter account), an edge is formed between them. The usefulness of network visualizations is to investigate trendsand events as a whole. The challenging part of analyzing a social network is identifying the nodes and relationships that are worth investigating further. An essential feature that makes social network analysis important for combating disinformation is that false news spreads faster than real news through online platforms involving many users and creating large networks [2]. For example, a CBC journalist [3] posted a wrong claim that identified the attacker of an attack in Toronto in 2018 as \"angry\" and \"Middle Eastern\" at the same time as another journalist who posted a claim correctly identifying the attacker as \"white\". It turned out that the misleading tweet identifying the attacker as Middle Eastern received far more engagement than the accurate one roughly five hours after the attack. A network emerged rapidly around the false claim, and users were quick to disseminate it. The visualization of a network involving many accounts and their interactions may reveal those accounts that try to influence the public with certain views. During critical events, such as the 2016 US presidential election and the outbreak of the COVID-19 pandemic, fabricated information was disseminated through social media to deceive the public. Several works revealed the roles of bots (i.e., automated accounts posing as real users) in the spread of misinformation [2; 4]. Their characteristics were excessive posting via the retweeting of emerging news and tagging or mentioning influential accounts in the hope they would spread the content to their thousands of followers [5]. A need to detect inauthentic users led to investigating the posting activities, interactions, and spreading behaviors. Network analysis and visualization techniques could be valuable for detecting such inauthentic accounts based on their behaviors in a network that differentiate them from those of real users. In this work, we present the MeVer NetworkX analysis and visualization tool. The tool's development was motivated by a need to follow complex social media conversations and to gain insights about how information is spreading in networks and how groups frequently communicate with each other and form communities. The tool falls in the scope of assisting journalistic practices and, more precisely, helping journalists retrieve specific and detailed information or form a comprehensive view around a complex online event or topic of discussion.