In this paper, we sketch a programme for AI driven social theory. We begin by defining what we mean by artificial intelligence (AI) in this context. We then lay out our model for how AI based models can draw on the growing availability of digital data to help test the validity of different social theories based on their predictive power. In doing so, we use the work of Randall Collins and his state breakdown model to exemplify that, already today, AI based models can help synthesize knowledge from a variety of sources, reason about the world, and apply what is known across a wide range of problems in a systematic way. However, we also find that AI driven social theory remains subject to a range of practical, technical, and epistemological limitations. Most critically, existing AI systems lack three essential capabilities needed to advance social theory in ways that are cumulative, holistic, open-ended, and purposeful. These are (1) semanticization, i.e., the ability to develop and operationalize verbal concepts to represent machine-manipulable knowledge, (2) transferability, i.e., the ability to transfer what has been learned in one context to another, and (3) generativity, i.e., the ability to independently create and improve on concepts and models. We argue that if the gaps identified here are addressed by further research, there is no reason why, in the future, the most advanced programme in social theory should not be led by AI-driven cumulative advances.

We begin by defining what we mean by artificial intelligence (AI) in this context. We then lay out our specification for how AI-based models can draw on the growing availability of digital data to help test the validity of different social theories based on their predictive power. In doing so, we use the work of Randall Collins and his state breakdown model to exemplify that, already today, AI-based models can help synthesise knowledge from a variety of sources, reason about the world, and apply what is known across a wide range of problems in a systematic way. However, we also find that AI-driven social theory remains subject to a range of practical, technical, and epistemological limitations. Most critically, existing AI-systems lack three essential capabilities needed to advance social theory in ways that are cumulative, holistic, open-ended, and purposeful.

Signed link prediction in social networks aims to reveal the underlying relationships (i.e. links) among users (i.e. nodes) given their existing positive and negative interactions observed. Most of the prior efforts are devoted to learning node embeddings with graph neural networks (GNNs), which preserve the signed network topology by message-passing along edges to facilitate the downstream link prediction task. Nevertheless, the existing graph-based approaches could hardly provide human-intelligible explanations for the following three questions: (1) which neighbors to aggregate, (2) which path to propagate along, and (3) which social theory to follow in the learning process. To answer the aforementioned questions, in this paper, we investigate how to reconcile the \textit{balance} and \textit{status} social rules with information theory and develop a unified framework, termed as Signed Infomax Hyperbolic Graph (\textbf{SIHG}). By maximizing the mutual information between edge polarities and node embeddings, one can identify the most representative neighboring nodes that support the inference of edge sign. Different from existing GNNs that could only group features of friends in the subspace, the proposed SIHG incorporates the signed attention module, which is also capable of pushing hostile users far away from each other to preserve the geometry of antagonism. The polarity of the learned edge attention maps, in turn, provide interpretations of the social theories used in each aggregation. In order to model high-order user relations and complex hierarchies, the node embeddings are projected and measured in a hyperbolic space with a lower distortion. Extensive experiments on four signed network benchmarks demonstrate that the proposed SIHG framework significantly outperforms the state-of-the-arts in signed link prediction.

Researchers have long converged that the evolution of a Social Networking Service (SNS) platform is driven by the interplay between users' preferences (reflected in user-item consumption behavior) and the social network structure (reflected in user-user interaction behavior), with both kinds of users' behaviors change from time to time. However, traditional approaches either modeled these two kinds of behaviors in an isolated way or relied on a static assumption of a SNS. Thus, it is still unclear how do the roles of users' historical preferences and the dynamic social network structure affect the evolution of SNSs. Furthermore, can jointly modeling users' temporal behaviors in SNSs benefit both behavior prediction tasks?In this paper, we leverage the underlying social theories(i.e., social influence and the homophily effect) to investigate the interplay and evolution of SNSs. We propose a probabilistic approach to fuse these social theories for jointly modeling users' temporal behaviors in SNSs. Thus our proposed model has both the explanatory ability and predictive power. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed model.

With the pervasion of social media, topic identification in short texts attracts increasing attention in  recent years. However, in nature the texts of social media are short and noisy, and the structures are sparse and dynamic, resulting in difficulty to identify topic categories exactly from online social media. Inspired by social science findings that preference consistency and social contagion are observed in social media, we investigate topic identification in short and noisy texts by exploring social context from the perspective of social sciences. In particular, we present a mathematical optimization formulation that incorporates the preference consistency and social contagion theories into a supervised learning method, and conduct feature selection to tackle short and noisy texts in social media, which result in a Sociological framework for Topic Identification (STI). Experimental results on real-world datasets from Twitter and Citation Network demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of social context in topic identification.