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<p>Topic evolution analysis is essential for uncovering, comprehending, and leveraging the underlying mechanisms affecting the development of topics. However, previous studies mainly examine topic evolution in isolation, neglecting topic interactions and the functional roles of topics, thus failing to unveil and explain the interactive relationship driving their evolution. In this study, we define and identify the function of topics via a large language model (LLM) and disclose a competitive-cooperative mechanism (CCM) by scrutinizing the spatial autocorrelation of topics. Specifically, we collected about 23 million papers in the field of computer science and utilized zero-shot learning to identify the four functions (i.e., <i>Problem</i>, <i>Method</i>, <i>Problem</i>&amp;<i>Method</i>, and <i>Other</i>) of topics. Subsequently, we used Word2Vec to generate topic embeddings, then investigated CCM among topics with the same and/or different functions by examining the global and local spatial autocorrelation of topic frequency within their semantic space. Our findings reveal that <i>Problem</i> topics cooperate when the solution of one problem facilitates the solving of another one, yet compete for researchers’ attention. <i>Method</i> topics cooperate through complementarity but compete as researchers’ preference for more efficient methods. Among <i>Problem</i> and <i>Method</i> topics, cooperation emerges from the use of diverse methods to tackle problems. With the innovative insight into topic evolution, this study not only develops a new method for topic term identification but also reveals global and local CCM across topics of different functions.</p>

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Disclosing the competitive-cooperative mechanism driving topic evolution from the perspective of topic function analysis

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Junying Chen,
  • Wei Lu,
  • Zhenzhen Xu,
  • Feiyang Chen,
  • Shengzhi Huang

摘要

Topic evolution analysis is essential for uncovering, comprehending, and leveraging the underlying mechanisms affecting the development of topics. However, previous studies mainly examine topic evolution in isolation, neglecting topic interactions and the functional roles of topics, thus failing to unveil and explain the interactive relationship driving their evolution. In this study, we define and identify the function of topics via a large language model (LLM) and disclose a competitive-cooperative mechanism (CCM) by scrutinizing the spatial autocorrelation of topics. Specifically, we collected about 23 million papers in the field of computer science and utilized zero-shot learning to identify the four functions (i.e., Problem, Method, Problem&Method, and Other) of topics. Subsequently, we used Word2Vec to generate topic embeddings, then investigated CCM among topics with the same and/or different functions by examining the global and local spatial autocorrelation of topic frequency within their semantic space. Our findings reveal that Problem topics cooperate when the solution of one problem facilitates the solving of another one, yet compete for researchers’ attention. Method topics cooperate through complementarity but compete as researchers’ preference for more efficient methods. Among Problem and Method topics, cooperation emerges from the use of diverse methods to tackle problems. With the innovative insight into topic evolution, this study not only develops a new method for topic term identification but also reveals global and local CCM across topics of different functions.