Investigating social media usage patterns and privacy awareness with composite data visualization

This thesis describes an investigation into the degree of awareness people have of their activity and audience on social media, and into the alignment of sharing expectations with actual sharing behavior. It is previously reported that people tend to share problematic posts on social media networks because they are not always aware of who can actually see their posts and other activity and do not always apply privacy settings effectively. We built a data collection tool that gathers social media data, like posts, connections, and private messages, from Facebook, Twitter, Instagram, and LinkedIn, and assembles a composite profile combining information from all four networks for visualization. We then conducted a user study evaluating people's data sharing patterns, audience perceptions, and data self-awareness on social media. We first surveyed participants to discover their own estimates of certain activity and visibility metrics like post type ratios, connection proportions by interaction frequency, and connections by presence on multiple networks; we then interviewed them with the aid of the tool's visualization to compare their answers with ones we computed from their collected data and gauge their reactions. Notably, we determined that participants tend to significantly overestimate the proportion of connections with whom they interact on social media, and we found that participants also have trouble recalling what types of posts they have made and how many people they share between networks; nevertheless, when presented with the actual computed information and a visualization of their social media activity and visibility, most participants reported being satisfied with their sharing strategy, although a minority did report a desire to change their behavior or re-examine their sharing settings. This document presents the methods used, the results from the user study, and suggestions and cautions for future work.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Yuan, Ben Z. (Ben Ze)

Advisor dc:contributor.advisor

• Hal Abelson and Ilaria Liccardi.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/113923

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/113923