Aqdas Malik

Habib Karbasian, Hemant Purohit, Rajat Handa et al.

Social media provides a mechanism for people to engage with social causes across a range of issues. It also provides a strategic tool to those looking to advance a cause to exchange, promote or publicize their ideas. In such instances, AI can be either an asset if used appropriately or a barrier. One of the key issues for a workforce diversity campaign is to understand in real-time who is participating - specifically, whether the participants are individuals or organizations, and in case of individuals, whether they are male or female. In this paper, we present a study to demonstrate a case for AI for social good that develops a model to infer in real-time the different user types participating in a cause-driven hashtag campaign on Twitter, ILookLikeAnEngineer (ILLAE). A generic framework is devised to classify a Twitter user into three classes: organization, male and female in a real-time manner. The framework is tested against two datasets (ILLAE and a general dataset) and outperforms the baseline binary classifiers for categorizing organization/individual and male/female. The proposed model can be applied to future social cause-driven campaigns to get real-time insights on the macro-level social behavior of participants.

Aqdas Malik

This dissertation is based on five empirical research articles investigating the different latent factors that motivate and hinder the process of digital-photo interaction in computer-mediated platforms. Study I examine the current practices surrounding digital photos in the context of personal photo repositories (N=15). Study II investigates the gratifications and impeding factors associated with photo-tagging activity on Facebook (N=67). Study III develops and tests an instrument for understanding the gratifications of Facebook photo-sharing (N=368). Study IV examines the impact of various aspects of privacy in relation to photo-sharing intentions on Facebook (N=378). Finally, study V investigates the age and gender differences regarding various aspects of privacy and trust in the context of photo-sharing activity on Facebook (N=378). The dissertation reveals the following findings: First, lack of "social features" is one of the essential reasons for non-acceptance of tagging feature in standalone photo management applications. Second, photo-sharing and photo-tagging adoption and popularity can be attributed to various factors such as affection, attention, communication, disclosure, habit, information sharing, self-expression, socialization, and social influence. Third, age, gender, and activity influence photo-sharing and photo-tagging gratifications. Fourth, in the context of Facebook photo-sharing, various aspects of privacy significantly impact users trust and activity and consequently photo-sharing intentions. Fifth, women and young Facebook users are significantly more concerned about the privacy of their shared photos. Sixth, privacy-protective measures are significantly exercised more by young Facebook users, yet they exhibit more trust and a higher level of activity on Facebook.