From Cancel Culture to the Metaverse: Detecting Trends in the Media Spotlight
By Janna Lipenkova  |  Mar 23, 2022
From Cancel Culture to the Metaverse: Detecting Trends in the Media Spotlight
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Computational linguist Janna Lipenkova discusses how trending topics in the media can be extracted using natural language processing by looking at prominent time series patterns, analyzing the meaning of a topic by constructing and inspecting its semantic context, and witnessing changes over time in the semantic context of a topic.

BERLIN -The modern world is changing at an unprecedented speed and intensity, creating a great deal of uncertainty for some and opportunities for others. As a business, how does one become part of the ‘opportunity’ camp? The first step is to gain a deep understanding of the changes that are happening. Based on this, one can match external changes to an internal strategy and competences and come up with a successful strategy for integration. 


“The only constant in life is change.” 

– Heraclitus, Ancient Greek philosopher


Trends are an important component of change. In a broad sense, trends exist on multiple levels. They can be new topics dominating the discussion and changing attitudes or narratives, as well as the ubiquitous megatrends - global tectonic shifts that affect all levels of humankind. While trusted networks such as one’s own workforce or industry experts can provide valuable insights on trends, public media plays a growing role in shaping and promoting them. 

This article will show how trending topics in media can be extracted and analyzed using natural language processing (NLP) in three steps:

- Step 1: Look at prominent time series patterns to spot trending topics.

- Step 2: Analyze the meaning of a topic by constructing and inspecting its semantic context.

- Step 3: Represent changes over time in the semantic context of a topic.

This article is part of a series on trend analysis from text data and the operationalization of these trends for business purposes.


Detecting Trending Topics Based on Prominence

Most topics can be represen

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