Google Trends to Investigate the Degree of Global Interest Related to Indoor Location Detection

Thakur, Nirmalya; Han, Chia Y.

doi:10.1007/978-3-030-85540-6_73

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…Using Google Trends, Ortiz-Martínez et al [68] showed that there was a high correlation between the COVID-19 incidence in Colombia and Google searches on COVID-19 in Colombia (R 2 = 0.8728 and p < 0.0001). In addition to the above, in the last few years, Google Trends has also had a wide range of interdisciplinary applications related to the understanding and analysis of public health concerns [69][70][71], societal problems [72][73][74], emerging technologies [75][76][77], human behavior analysis [78][79][80][81], assistive technologies [82][83][84][85], humanitarian issues [86][87][88][89], and smart technologies [90][91][92][93]. Therefore, it may be concluded that prior works in this field have focused on using Google Trends related to mining, analysis, and investigation of multimodal components of web behavior for a wide range of applications and use cases, with a specific focus on studying and analyzing web behavior during various virus outbreaks.…”

Section: Literature Reviewmentioning

confidence: 99%

A Large-Scale Dataset of Search Interests Related to Disease X Originating from Different Geographic Regions

Thakur,

Cui,

Patel

et al. 2023

Data

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The World Health Organization (WHO) added Disease X to their shortlist of blueprint priority diseases to represent a hypothetical, unknown pathogen that could cause a future epidemic. During different virus outbreaks of the past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from various disciplines utilized Google Trends to mine multimodal components of web behavior to study, investigate, and analyze the global awareness, preparedness, and response associated with these respective virus outbreaks. As the world prepares for Disease X, a dataset on web behavior related to Disease X would be crucial to contribute towards the timely advancement of research in this field. Furthermore, none of the prior works in this field have focused on the development of a dataset to compile relevant web behavior data, which would help to prepare for Disease X. To address these research challenges, this work presents a dataset of web behavior related to Disease X, which emerged from different geographic regions of the world, between February 2018 and August 2023. Specifically, this dataset presents the search interests related to Disease X from 94 geographic regions. These regions were chosen for data mining as these regions recorded significant search interests related to Disease X during this timeframe. The dataset was developed by collecting data using Google Trends. The relevant search interests for all these regions for each month in this time range are available in this dataset. This paper also discusses the compliance of this dataset with the FAIR principles of scientific data management. Finally, an analysis of this dataset is presented to uphold the applicability, relevance, and usefulness of this dataset for the investigation of different research questions in the interrelated fields of Big Data, Data Mining, Healthcare, Epidemiology, and Data Analysis with a specific focus on Disease X.

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Section: Literature Reviewmentioning

confidence: 99%

A Large-Scale Dataset of Search Interests Related to Disease X Originating from Different Geographic Regions

Thakur,

Cui,

Patel

et al. 2023

Data

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“…Leveraging the power of text mining, health-related data can be gleaned from platforms like Twitter [45][46][47]. The comprehensive information contained within Twitter data grants researchers access to different forms of web behavior on the internet [48,49] and user-generated content [50,51], facilitating early response strategies and informed decision-making. The realm of social media mining assumes a pivotal role in monitoring diseases and gauging public awareness of health concerns, thereby enabling proactive disease forecasting [52].…”

Section: Relevance Of Mining and Analysis Of Social Media Data During...mentioning

confidence: 99%

Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis

Thakur

2023

ASI

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This paper presents multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between 25 May 2020 and 31 January 2023. First, the analysis shows that the average number of Tweets per month wherein individuals self-reported Long COVID on Twitter was considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings from sentiment analysis using VADER show that the percentages of Tweets with positive, negative, and neutral sentiments were 43.1%, 42.7%, and 14.2%, respectively. To add to this, most of the Tweets with a positive sentiment, as well as most of the Tweets with a negative sentiment, were not highly polarized. Third, the result of tokenization indicates that the tweeting patterns (in terms of the number of tokens used) were similar for the positive and negative Tweets. Analysis of these results also shows that there was no direct relationship between the number of tokens used and the intensity of the sentiment expressed in these Tweets. Finally, a granular analysis of the sentiments showed that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively.

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“…Twitter is the most used social media platform amongst journalists [5] and ranks amongst the most popular social media platforms on a global scale [6]. Twitter has been highly popular amongst data scientists and computer science researchers for studying, analyzing, modeling, and interpreting social media communications related to various topics, such as ChatGPT [7], the Russia-Ukraine war [8], cryptocurrency markets [9], virtual assistants [10], mental health [11], loneliness in the elderly [12], housing needs of low-income families [13], animal welfare [14], climate change [15], cognitive impairment [16], the electronics industry [17], agriculture [18], race and ethnicity [19], fake news [20], abortion [21], religion [22], fall detection [23,24], gender identity [25], elections [26], politics [27], food insufficiency [28], pregnancy [29], drug safety [30], indoor localization [31], gambling [32], education systems [33], exoskeletons [34], personalized medicine [35], natural disasters [36], crimes [37], democracy [38], and transportation [39], just to name a few. In addition to the above, Twitter data mining and analysis has also attracted the attention of healthcare researchers, epidemiologists, and medical practitioners, as is evident from several works that focused on the mining and analysis of tweets related to pandemics, epidemics, viruses, and diseases such as Ebola [40], E-Coli [...…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox

Thakur

2023

BDCC

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Mining and analysis of the big data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of tweets related to Ebola, E-Coli, Dengue, Human Papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson’s, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as “catalysts” for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both of these viruses. None of the prior works in this field analyzed tweets focusing on both COVID-19 and MPox simultaneously. To address this research gap, a total of 61,862 tweets that focused on MPox and COVID-19 simultaneously, posted between 7 May 2022 and 3 March 2023, were studied. The findings and contributions of this study are manifold. First, the results of sentiment analysis using the VADER (Valence Aware Dictionary for sEntiment Reasoning) approach shows that nearly half the tweets (46.88%) had a negative sentiment. It was followed by tweets that had a positive sentiment (31.97%) and tweets that had a neutral sentiment (21.14%), respectively. Second, this paper presents the top 50 hashtags used in these tweets. Third, it presents the top 100 most frequently used words in these tweets after performing tokenization, removal of stopwords, and word frequency analysis. The findings indicate that tweets in this context included a high level of interest regarding COVID-19, MPox and other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that compares the contributions of this paper with 49 prior works in this field is presented to further uphold the relevance and novelty of this work.

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Google Trends to Investigate the Degree of Global Interest Related to Indoor Location Detection

Cited by 5 publications

References 16 publications

A Large-Scale Dataset of Search Interests Related to Disease X Originating from Different Geographic Regions

A Large-Scale Dataset of Search Interests Related to Disease X Originating from Different Geographic Regions

Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis

Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox

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