Date Published: July 11, 2018
Publisher: Public Library of Science
Author(s): Zahra Riahi Samani, Sharath Chandra Guntuku, Mohsen Ebrahimi Moghaddam, Daniel Preoţiuc-Pietro, Lyle H. Ungar, Kim-Kwang Raymond Choo.
Assessing the predictive value of different social media platforms is important to understand the variation in how users reveal themselves across multiple platforms. Most social media platforms allow users to interact in multiple ways: by posting content to the platform, liking others’ posts, or building a user profile. While prior studies offer insights into how language use differs across platforms, differences in image usage is less well understood. In this study, we analyzed variation in image content with user personality across three interaction types (posts, likes and profile images) and two platforms, using a unique data set of users who are active on both Twitter and Flickr. Usage patterns on these two social media platforms revealed different aspects of users’ personality. Cross-platform data fusion is thus shown to improve personality prediction performance.
According to a Pew Research study , 56% of US adults online use more than one social media platform. While some of these, such as LinkedIn have a specific use , other platforms such as Twitter are used in diverse ways by different groups of users . Also, there are multiple ways in which users can interact with a social media platform—either by posting content to the platform, liking content that others have posted or maintaining up their user profile.
With proliferation of mobile technologies and image sharing platforms, sharing pictures is the most commonly action (82% of the American users), followed by exchanging text messages (80% of the users) and accessing the Internet (56% of the users) . In other words, “photos have become an important social content online [10, 11] that and can serve as a substitute for more direct forms of interaction like email .
We use two data sets in our experiments. The first data set contains a set of Flickr users with their self-assessed personality traits. This data set is used to compare the predictive power of various image interactions of these users on Flickr. The second data set is built for this study and consists of users who have active accounts both on Twitter and Flickr. Personality traits for this group are estimated by analyzing their online text. Image interactions of these users on both platforms are used in cross-modal and cross-platform analysis. Figs 1 and 2 show the process of our cross-modal and cross-platform analysis. We also compare different features in predicting personality traits and perform experiments to uncover if cross-modal and cross-platform data fusion can improve the predictive accuracy of personality. In the rest of this section, we describe in more detail the data sets used in the analysis, the methods for obtaining the features used in our results and the methodology for predicting personality traits.
In this section we answer the research questions raised in the Introduction.
The results of this work confirm the hypothesis that multiple interactions that users have with social media platforms such as choosing profile pictures, posting and liking images have predictive utility for automatic personality assessment of users, albeit with varying levels of performance; and combining different interaction types and platforms, although it involves more computation, can boost the prediction results. While posted images topped the performance in predicting personality followed by liked images and then profile pictures, profile pictures are a ubiquitous way for users to present themselves on social media, and they are usually considered public data which makes them easier to be accessed by automatic algorithms. Posted and liked images, on the other hand, are relatively more diverse in their content and automatic algorithms would need access to a larger set of such images across user’s posting timeline than liked pictures to make accurate predictions.
We carried out a cross-modal and cross-platform study using images posted on social media. We used a wide range of color and semantic features extracted from images to analyze how different features can be applied to predict Big Five personality traits.