Author: Tanner Jordan Bergeron

In the time leading up to the election in the United States, there is an increasing concern about the validity of the information being thrown around by candidates, senate members, and other people involved with the election process on both sides of the political spectrum. As we learned in class, when mapping out a friendship network, there are often many small clusters of communities that communicate frequently and longer distance weak edges connecting these small communities together. If for instance we can modify this model to instead represent outgoing edges as tweet mentions, and incoming edges as getting mentioned in someone’s tweet. This is exactly one of the types of models featured in Mozilla’s new Social Media Analysis Tool released back in September of 2020.

What is the Social Media Analysis Tool (SMAT)?
SMAT (www.smat-app.com) is a free, open-source, and easy to use tool to analyze online conversations on platforms like Twitter, Reddit, and even 4Chan. SMAT scrapes these websites and populates it’s database to allow it to query for important information very quickly. Users can study everything from what’s trending during a certain period of time, to who is driving the conversation, to what links are being shared the most, and beyond. For example, a researcher looking into COVID conspiracies can determine when the conspiracy began, who is leading the conversation, and which news stories, blog posts, and other links are appearing most frequently in the online conversation.

Why was it created?
It was designed to help facilitate activists, journalists, researchers, and other social good organizations to analyze and visualize larger trends on a variety of platforms. It’s often difficult to get a good idea of the structure of a network like twitter just from following links to twitter accounts so being able to consult a tool like this not only makes it much easier, but is open to anyone to aid in the identification of disinformation.

The great thing about SMAT is that it is general enough that it is able to be applied to a wide variety of topics. The models shown are specifically tailored to the coming election, but in general you can use it for many things. If you’d like to try it out for yourself, you can play around with it here (www.smat-app.com)

Bibliography

• https://foundation.mozilla.org/en/blog/new-open-source-tool-tracking-disinformation/
• https://www.smat-app.com/

Sites like Facebook allow us to stay connected and maintain friendships with hundreds and sometimes thousands of people at a time but it can often feel like these friendships aren’t really true friendships at all. In 2016, Dr. Dunbar, an evolutionary sociologist and cognition of the University of Oxford, published a paper posing the question of whether social media is able to prop up the size of offline social networks which have previously been shown to be constrained by our cognitive abilities and in part by the time costs of servicing such relationships.

Results from 3373 users on Facebook between 18 and 65 years old were studied which on average had 150 friends. Of those 150, 4.1 were considered to be dependable, and 13.6 were considered to express sympathy during an “emotional crisis”. In Dunbar’s earlier work, these numbers align closely with the figures calculated from studying “offline” friendships.

In his paper he notes “Respondents who had unusually large networks did not increase the numbers of close friendships they had, but rather added more loosely defined acquaintances into their friendship circle.” The ease of creating friendships in these online environments makes it difficult to invest in maintaining an essential level of “emotional intensity”

This figure shows the frequency distributions of what people identified were the number of “close” friends in their support clique and the number of friends in their sympathy group they had.

This information is interesting because in class we have talked about how in friend graphs there are usually tightly coupled components of “strong” friendships of which are connected to other tightly coupled components by weak edges which represent a more distant friendship. This paper makes the important observation that the graph representations of this online friend data and that of the real friendship graph made up of your offline friendships, are statistically similar and analogous and that just because you may have 150 Facebook friends, doesn’t mean they necessarily contribute to what you would consider your real friends. In other words, it seems that this study agrees with Dunbar’s previous research about the limits and constraints of maintaining human relationships.

Source: https://royalsocietypublishing.org/doi/full/10.1098/rsos.150292