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Intermediary topic modelling analysis results: Mapping the tech world using text mining methods

Authors: Kristóf Gyódi; Łukasz Nawaro; Michał Paliński;

Intermediary topic modelling analysis results: Mapping the tech world using text mining methods

Abstract

This study presents an innovative methodology for analysing technology news using various text mining methods. News articles provide a rich source of information to track promising emerging technologies, relevant social challenges or policy issues. Our goal is to support the Next Generation Internet initiative by providing data-science tools to map and analyse the developments of the tech word. Based on more than 200 000 articles from major media outlets, we are going to identify widely discussed topics, focusing on emerging technologies and policy issues and dive deeper in selected areas and highlight key focal points of recent developments. To meet these goals, a number of machine learning techniques are combined. The major steps can be summarised as follows: ● 17 general umbrella topics are explored ● 5 topics are selected for further analysis ● Deep dives are presented with 2D interactive maps More specifically, the topics selected for the deep dives are: 1. AI and Robots 2. Policy (sums up 3 relevant areas) 3. Media 4. Business 5. Cybersecurity With the Policy topic grouping together 3 areas: Social media crisis, Privacy and 5G.

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Keywords

Human-centric, future, technology, data-driven, policy, collective intelligence, news

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green