Social Network Analysis: The Samurai Way
Social Network Analysis (SNA) is the mapping and measuring of relationships between people, groups, organizations, or other entities. It has been around since the 20th Century. Social Media has just amplified the scale of it. It is important for identifying associates of entities, understanding clusters of the associates, and verifying the metadata of known clusters. SNA provides both a visual and mathematical analysis of human relationships. One way to do it is through Krackhardt Kite Graph or Knoke’s Information Exchange Direct Graph. This requires a more manual and physical approach. Other manual methods are through DataVis Tool by OSINTCombine or Gephi.
The Fundamental properties to understand before diving into Social Network Analysis are:
- Actor: The core entity which can be an individual, corporate, or a group. More commonly denoted as a Node.
- Relational Tie: The social tie between two actors. It can be friendship, affiliation, formal, biological, etc. Usually denoted as a Link.
- Dyad: The relationship between two actors
- Triad: The relationship between three actors
- Group: Collection of actors bounded by a common property as a set. Also known as Social Circle.
- Centrality: It shows the “Importance” of a particular node.
Social Media Perspective
The two common types of Analysis done on Social Media:
- Based on Engagement: Usually, the analysis was done based on the engagement of a user that is, likes, comments, replies, etc. We can easily identify associates, and engagements and can be easy to analyze the frequency. We can also see Hidden Friends through methods and it may highlight common “touch-point” between triads.
- Based on Friends List: It’s usually a complex network, hence it’s easier to enumerate and also tends to scale quickly for multi-user. It highlights common key entities between triads.
Deeper dive into Engagement and Friends Collection
To do an Engagement collection of Facebook, we will have to access mbasic.facebook.com and start scraping likes from pictures. Adding this to an Excel sheet and saving this as a .csv file will help us visualize the data produced from scraping. Using something like Gephi or DataVis from OSINTCombine for the collected data visualization aids us in understanding. Similarly, we can do an engagement collection of Twitter through TweetBeaver. We will have to download the user’s favorite and it will scrape the engagement (likes) of the entered user, compile it in a .csv file. As for Instagram, we will have to utilize a website called Spatulah. It collects user related comments made. To collect data off LinkedIn, we can use Phantombuster.
To do friends collection on Facebook, we will have to access mbasic.facebook.com/[user]/friends and start scraping their friends’ list. Adding this to an Excel sheet and saving this as a .csv file as before. Similarly, we can do an engagement collection of Twitter through TweetBeaver. We will have to download the user friends list and it will scrape it and compile it in a .csv file. As for Instagram, we will use a scraper extension of Chrome and the site Instagram.com/[user].
We can repeat this as many times as we like on the same Visual Graphing platform in order to make a Cross-Platform Social Network Visual Map.
Conclusion
Social network analysis is a practical method that can reliably help investigators in understanding the User, group, or Organization, their interactions and relations. Using SNA can reveal important information about the user, such as engagements and friends. On the group level, it renders a more detailed picture of the group and the participants. Using mathematical parameters can also help add insights into the level of activity in a precise way.
