Modeling text embedded information cascades

Abstract

Networks mediate several aspects of society. For example, social networking services (SNS) like Twitter and Facebook have greatly helped people connect with families, friends and the outside world. Public policy diffuses over institutional and social networks that connect political actors in different areas. Inferring network structure is thus essential for understanding the transmission of ideas and information, which in turn could answer questions about communities, collective actions, and influential social participants. Since many networks are not directly observed, we often rely on indirect evidence, such as the timing of messages between participants, to infer latent connections. The textual content of messages, especially the reuse text originating elsewhere, is one source of such evidence. This thesis contributes techniques for detecting the evidence of text reuse and modeling underlying network structure. We propose methods to model text reuse with accidental and intentional lexical and semantic mutations. For lexical similarity detection, an n-gram shingling algorithm is proposed to detect "locally" reused passages, instead of near-duplicate documents, embedded within the larger text output of network nodes. For semantic similarity, we use an attention based neural network to also detect embedded reused texts. When modeling network structure, we are interested in inferring different levels of details: individual links between participants, the structure of a specific information cascade, or global network properties. We propose a contrastive training objective for conditional models of edges in information cascades that has the flexibility to answer those questions ii Abstract iii and is also capable of incorporating rich node and edge features. Last but not least, network embedding methods prove to be a good way to learn the representations of nodes while preserving structure, node and edge properties, and side information. We propose a self-attention Transformer-based neural network trained to predict the next activated node in a given cascade to learn node embeddings.