Annals of Library and Information Studies

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Exploring Online Community Search Interest on Temporal Dynamics of Ayodhya:A Case Study of the Ram Mandir based on Google Search Trends Insight


Affiliations
1 Assistant Professor, Department of Library & Information Science, Babasaheb Bhimrao Ambedkar University, Lucknow, 226025, India
2 Associate Professor, Department of Statistics, Babasaheb Bhimrao Ambedkar University, Lucknow, 226025, India
3 Professor, Department of Library & Information Science, Mizoram University, Aizawl, 796004, India

In the digital age, online platforms have become essential tools for understanding societal interests and trends. Google Trends, a widely used platform, provides valuable insights into search behaviour by tracking the popularity of specific search terms over time and across geographic locations. The present paper explores the utility of Google Trends in analysing the search interest in the "Ram Mandir" topic, particularly focusing on its relevance in understanding societal sentiments, public discourse, and information-seeking behaviours. Ram Mandir's temporal variations in online search interest are examined through Google Trends data from 2008 to 2023. The objectives include examining variations in search interest across different periods, forecasting temporal trends, analysing geographical variations, and identifying relevant search queries and topics. Statistical and machine learningmodels were employed for trend forecasting, including AutoARIMA, Random Forest, Support Vector Regression (SVR), and Neural Network. The study revealed consistent but intermittent fluctuations in search interest within India, with Support Vector Regression demonstrating superior performance. Conversely, global interest remained steady, reflecting sustained but moderate engagement. Geographically, regions such as Jharkhand, Uttar Pradesh, and Madhya Pradesh showed the highest search interest, possibly linked to cultural or historical connections. Analysis of related search queries unveiled multifaceted dimensions, encompassing religious, historical, political, and infrastructure themes. Furthermore, this study addresses research gaps by utilising Google Trends to investigate cultural phenomena and religious landmarks, offering insights into digital representations of cultural discourse.This study contributes to understanding the interplay between digital media, culture, and society, shedding light on the evolving nature of public discourse in the digital era.

Keywords

Google Trends, Ram Mandir, Temporal Dynamics, Search Interest, Cultural Phenomena, Statistical Models and Machine Learning.
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  • Exploring Online Community Search Interest on Temporal Dynamics of Ayodhya:A Case Study of the Ram Mandir based on Google Search Trends Insight

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Authors

Nitesh Kumar Verma
Assistant Professor, Department of Library & Information Science, Babasaheb Bhimrao Ambedkar University, Lucknow, 226025, India
Subhash Kumar Yadav
Associate Professor, Department of Statistics, Babasaheb Bhimrao Ambedkar University, Lucknow, 226025, India
Manoj Kumar Verma
Professor, Department of Library & Information Science, Mizoram University, Aizawl, 796004, India

Abstract


In the digital age, online platforms have become essential tools for understanding societal interests and trends. Google Trends, a widely used platform, provides valuable insights into search behaviour by tracking the popularity of specific search terms over time and across geographic locations. The present paper explores the utility of Google Trends in analysing the search interest in the "Ram Mandir" topic, particularly focusing on its relevance in understanding societal sentiments, public discourse, and information-seeking behaviours. Ram Mandir's temporal variations in online search interest are examined through Google Trends data from 2008 to 2023. The objectives include examining variations in search interest across different periods, forecasting temporal trends, analysing geographical variations, and identifying relevant search queries and topics. Statistical and machine learningmodels were employed for trend forecasting, including AutoARIMA, Random Forest, Support Vector Regression (SVR), and Neural Network. The study revealed consistent but intermittent fluctuations in search interest within India, with Support Vector Regression demonstrating superior performance. Conversely, global interest remained steady, reflecting sustained but moderate engagement. Geographically, regions such as Jharkhand, Uttar Pradesh, and Madhya Pradesh showed the highest search interest, possibly linked to cultural or historical connections. Analysis of related search queries unveiled multifaceted dimensions, encompassing religious, historical, political, and infrastructure themes. Furthermore, this study addresses research gaps by utilising Google Trends to investigate cultural phenomena and religious landmarks, offering insights into digital representations of cultural discourse.This study contributes to understanding the interplay between digital media, culture, and society, shedding light on the evolving nature of public discourse in the digital era.

Keywords


Google Trends, Ram Mandir, Temporal Dynamics, Search Interest, Cultural Phenomena, Statistical Models and Machine Learning.