The emergence of social networks and social media has resulted in exponential increase in the amount of data that link diverse types of richly structured digital objects e.g., individuals, articles, images, videos, music, etc. Such data are naturally represented as heterogeneous networks with multiple types of objects e.g., actors, video, postings, images, etc. and multiple types of links e.g., links that connect actors to items e.g., photos, videos, articles that denote relationships between individuals and items, and links between actors that denote social ties e.g., friendship, etc. Such data present a number of research challenges in machine learning, network analysis, and causal discovery. The data are incomplete, heterogeneous, highly sparse, and exhibit complex relationships and temporal structure. It is often useful to associate with each actor or individual, one or more labels that represent the memberships of the actor in specific groups e.g., political, social, or religious groups. Labels of actors in a social network can be used in a variety of ways: recommending specific items (e.g., music, movies), activities. Furthermore, labels or states of actors change over time due to the temporal nature of the real-world social networks and social media. Uncovering the causal accounts of the state dynamics of actors has an important impact in explaining why people change their preference, emotion, or interest in a specific activity and in predicting actors' future behaviors. Against this background, this dissertation aims to develop (i) novel machine learning approaches for labeling actors in social networks, with particular emphasis on methods for dealing with a diversity of actors, objects, and relationships and with data sparsity; (ii) uncovering the causal accounts of the social network and social media dynamics (with emphasis on causal accounts of sentiment change and related applications). The introduced algorithms are implemented and experimentally evaluated on a number of real-world data sets. The proposed methodologies for actor labeling and analysis of causal accounts have broad applicability in a variety of areas such as bioinformatics and neuroscience. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]