D yna M i TE : Discovering Explosive Topic Evolutions with User Guidance (2023)

Abstract: Dynamic topic models (DTMs) analyze text streams to capture the evolution of topics. Despite their popularity, existing DTMs are either fully supervised, requiring expensive human annotations, or fully unsupervised, producing topic evolutions that often do not cater to a user’s needs. Further, the topic evolutions produced by DTMs tend to contain generic terms that are not indicative of their designated time steps. To address these issues, we propose the task of discriminative dynamic topic discovery. This task aims to discover topic evolutions from temporal corpora that distinctly align with a set of user-provided category names and uniquely capture topics at each time step. We solve this task by developing DynaMiTE, a framework that ensembles semantic similarity, category indicative, and time indicative scores to produce informative topic evolutions. Through experiments on three diverse datasets, including the use of a newly-designed human evaluation experiment, we demonstrate that DynaMiTE is a practical and efficient framework for helping users discover high-quality topic evolutions suited to their interests.

## introduction dynamic topic models (dtms) seek to capture the evolution of topics in time-stamped documents @xcite . these models can be applied to many downstream tasks, including studying breakthroughs in scientific research @xcite , discovering global issues in parliamentary debates @xcite , and tracking evolving news stories @xcite @xcite . as information and language continuously evolve, dtms are @xcite for topics natural language processing (nlp) and neural networks (nns) on arxiv machine learning papers, compared to our output. first, unsupervised dtms fail to consider their users' needs, such as specific topics or categories of interest. 2 hence, the discovered topics may not be completely interpretable or relevant to the user @xcite . for example in second, unsupervised dtms fail to distinguish between terms that are generic and terms that are distinct to each time step. for example in to address the above shortcomings, we introduce a new task, discriminative dynamic topic discovery, which aims to create informative topic evolutions suited to a user's needs. we minimally represent a user's interests as a set of provided category names or seeds, i.e., terms present in the input corpus. a discriminative dynamic topic discovery framework must produce evolving topics for each seed that are distinctly relevant to the category and time step. for this task, we develop dynamite, an iterative framework to dynamically mine topics with category seeds. avoiding the pitfalls of existing dtms, dynamite combines three scores to ensure that candidate terms are (1) semantically similar to a user's interests, (2) popular in documents indicative of the user-specified category, and (3) indicative of the corresponding time step. we briefly describe these scores as follows: dynamite ensembles these three scores after every training iteration to mine a single term for each time step and each category ( §4.4). these terms are used to refine the discriminative dynamic word embeddings and category indicative document retrieval, resulting in informative topic evolutions. we present dynamite as a fast, simple, and effective tool for aiding trend and evolution exploration. our contributions can be summarized as follows: ## related work we outline two variations on topic mining which incorporate time and user guidance, respectively. ## problem definition we define discriminative dynamic topic discovery as follows: given a corpus of time-stamped document collections @xmath0, d 2 , ..., d t } and a set of user-provided seeds @xmath1, c 2 , ..., c n }, discriminative dynamic topic discovery aims to retrieve topic evolutions {s tj } t @xmath2. the topic s tj contains a list of terms {w 1 , w 2 , ..., w m } that are discriminatively relevant to time t and category c j . the time steps @xmath3, ..., t } are any ordinal measure of time and can vary depending on the granularity required. ## methodology to solve discriminative dynamic topic mining, we propose dynamite, which iteratively populates each topic s tj . each topic s tj initially contains just the category name c j , and after every training iteration of dynamite, we expand each s tj with a single term w. for a term w to be added to s tj , we require three conditions to be satisfied: (1) w must be semantically similar to s tj ; (2) w must be prevalent in documents which discuss s tj ; (3) w must be a time indicative word of time t. we achieve these three goals by calculating three respective scores for candidate terms, namely semantic similarity scores with discriminative dynamic word embeddings ( §4.1), category indicative scores from retrieved category indicative documents ( §4.2), and time indicative scores based on topic burstiness ( §4.3). combining these scores ( §4.4), we can iteratively mine terms and use this information to further enrich our framework, illustrated in figure 1 and detailed in algorithm 1. ## experimental setup we present a detailed setup in appendix b. ## conclusion we propose the new task of discriminative dynamic topic discovery and develop dynamite to solve the task. through experiments on three diverse datasets, including the design of a new human evaluation experiment, we demonstrate that dyna-mite produces high-quality topic evolutions and outperforms state-of-the-art dtms. ablation studies show that dynamite effectively addresses a user's needs by retrieving category and time indicative terms. through runtime analyses, we find that dynamite is a computationally efficient and practical tool. finally, we probe the discrimina-tive dynamic embedding space of dynamite to identify key shifts in computer science, politics and news.