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        <title>Tracking the Evolution of Foresight Signals in News Data: The Case of the European Electric Vehicle Market</title>
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            <namePart type="given">Juan</namePart>
            <namePart type="given">Diego</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>To prepare for an uncertain future, organizations must continuously monitor emerging trends and early signals of change. The increasing availability of web-based textual data has boosted natural language processing (NLP) methods in strategic foresight, particularly in the scanning phase. While prior studies have extensively focused on the identification of signals in such data, considerably less attention has been paid to how these signals evolve over time and gain relevance as they become more visible. This study addresses this gap by examining whether tracking the temporal dynamics of signals can improve their assessment for strategic decision-making. Demonstrated on the use case of the European electric vehicle market, we find three dominant signal trajectories and show that burst dynamics tend to surface signal consolidation rather than the early detection of weak signals. The results indicate that foresight research should move beyond static, one-off analyses toward a dynamic temporal perspective capable of identifying signals at earlier stages of emergence.</abstract>
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%0 Conference Proceedings
%T Tracking the Evolution of Foresight Signals in News Data: The Case of the European Electric Vehicle Market
%A Navasartian, Karine
%Y T.Y.S.S., Santosh
%Y Rodriguez, Juan Diego
%Y de Gibert, Ona
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-393-7
%F navasartian-2026-tracking
%X To prepare for an uncertain future, organizations must continuously monitor emerging trends and early signals of change. The increasing availability of web-based textual data has boosted natural language processing (NLP) methods in strategic foresight, particularly in the scanning phase. While prior studies have extensively focused on the identification of signals in such data, considerably less attention has been paid to how these signals evolve over time and gain relevance as they become more visible. This study addresses this gap by examining whether tracking the temporal dynamics of signals can improve their assessment for strategic decision-making. Demonstrated on the use case of the European electric vehicle market, we find three dominant signal trajectories and show that burst dynamics tend to surface signal consolidation rather than the early detection of weak signals. The results indicate that foresight research should move beyond static, one-off analyses toward a dynamic temporal perspective capable of identifying signals at earlier stages of emergence.
%U https://aclanthology.org/2026.acl-srw.28/
%P 352-362

Markdown (Informal)

[Tracking the Evolution of Foresight Signals in News Data: The Case of the European Electric Vehicle Market](https://aclanthology.org/2026.acl-srw.28/) (Navasartian, ACL 2026)

• Tracking the Evolution of Foresight Signals in News Data: The Case of the European Electric Vehicle Market (Navasartian, ACL 2026)

ACL

• Karine Navasartian. 2026. Tracking the Evolution of Foresight Signals in News Data: The Case of the European Electric Vehicle Market. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 352–362, San Diego, California, United States. Association for Computational Linguistics.