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Use Of Google Trends In Determining Romanian Public Opinion Towards English Language Courses

Author

Listed:
  • Gyongyver Măduța

    (Romanian American University in Bucharest)

Abstract

Public interest towards foreign language courses is a major factor in influencing consumer behaviour, university policy and even public policy towards a more educated population. By using a “big data” approach, freely available through the Google Trends service, an assessment was made with regards to Romanian public interest towards English language courses available on the market, as well as the regions with the highest demand for English as a second language over the past five years. The results are relevant to academia, language centres, cultural centres, English schools and Universities with English- taught programmes.

Suggested Citation

  • Gyongyver Măduța, 2023. "Use Of Google Trends In Determining Romanian Public Opinion Towards English Language Courses," Romanian Economic Business Review, Romanian-American University, vol. 18(1), pages 41-46, March.
    • Handle: RePEc:rau:journl:v:18:y:2023:i:1:p:41-46
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    File URL: http://www.rebe.rau.ro/RePEc/rau/journl/SP23/REBE-SP23-A4.pdf
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    References listed on IDEAS

    as
    1. Wolfram Höpken & Tobias Eberle & Matthias Fuchs & Maria Lexhagen, 2019. "Google Trends data for analysing tourists’ online search behaviour and improving demand forecasting: the case of Åre, Sweden," Information Technology & Tourism, Springer, vol. 21(1), pages 45-62, March.
    2. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    Full references (including those not matched with items on IDEAS)

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