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In textual conversation threads, as found on many popular social mediaplatforms, each particular user text comment either originates a new thread ofdiscussion, or replies to a previous comment. An individual who makes anoriginal comment ---termed as the "root source''---is a topic initiator or evenan information source, and identifying such individuals is of particularinterest. The reply structure of comments is not always available (e.g. in theproliferation of a news event), and thus identifying root sources is anontrivial task. In this paper, we develop a generative model based on markedmultivariate Hawkes processes, and introduce a novel concept, "root sourceprobability", to quantify the uncertainty in attributing possible root sourcesto each comment. A dynamic-programming-based algorithm is then derived toefficiently compute root source probabilities. Experiments on synthetic andreal-world data show that our method identifies root sources that match groundtruth and human intuition.

Who Started It? Identifying Root Sources in Textual Conversation Threads