Developing Data Collection Tools for Measuring the User Behavior in Facebook

Abstract

Facebook is the most popular Online Social Network (OSN) worldwide. It has grown from one billion users in the third quarter of 2012 to two billions in the second quarter of 2017. The usage of Facebook has developed from contacting friends to share personal information, and it also became away for people to share, recommend, and link together all kinds of information. Facebook provides a huge set of behavioral data that if analyzed will produce different kinds of useful information that could be benefited from in understanding human behavior in depth, and impact design choices of social networks and applications. In addition to that, analyzing these information help users to discover what they use Facebook for, and how much time they spend using it. However, user behavior is not understood enough yet, because accessing this type of data is limited. By behavioral data we mean how the user interacts with Facebook functionalities. For example, how many likes, comments, shares and other similar functions, in addition to the content these functions applied to. In other words, we need to collect user interactions with which content together with respective timing information. In particular, we focus on four types of behavioral data: user’s actions, user’s friend list, activity log, and basic demographical information. In this project we aim to update and redesign an outdated software tools that were used in some previous similar studies [1]and [2]. Towards that end, we develop two software tools: First, Facebook Privacy Watcher and Analyzer (FPWA) which is a Browser extension (plugin) -specifically for chrome-. Second, Facebook Activity Log Collector (FALC) which is Android application for smartphones’ users. The plugin collects the data of participating users and stores it temporarily in the Chrome browser storage. This enables us to make these data viewable by the user who can then decide whether to send it or not to our server. In the same manner, the Android application also collects user actions, but only the actions which stored in the activity log of Facebook account. We have a small number of users who participate in our project, according to that, the analysis results won’t be comprehensive and representative. In spite of that, we develop a Python program that analyzes any size of samples regardless of the amount of our collected samples