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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
Image courtesy of and under license from Shutterstock.com
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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