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Social Trend Mining: Lead or Lag

Hossein Hassani
Nadejda Komendantova
Elena Rovenskaya
Mohammad Reza Yeganegi
International Institute for Applied Systems Analysis (IIASA), Schloßpl. 1, 2361 Laxenburg, Austria
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2023, 7(4), 171;
Submission received: 21 July 2023 / Revised: 24 October 2023 / Accepted: 2 November 2023 / Published: 7 November 2023


This research underscores the profound implications of Social Intelligence Mining, notably employing open access data and Google Search engine data for trend discernment. Utilizing advanced analytical methodologies, including wavelet coherence analysis and phase difference, hidden relationships and patterns within social data were revealed. These techniques furnish an enriched comprehension of social phenomena dynamics, bolstering decision-making processes. The study’s versatility extends across myriad domains, offering insights into public sentiment and the foresight for strategic approaches. The findings suggest immense potential in Social Intelligence Mining to influence strategies, foster innovation, and add value across diverse sectors.

1. Introduction

The importance of social trend mining lies in its ability to provide stakeholders with a deep understanding of user behavior, preferences, and sentiments. By leveraging the vast amount of data available from the internet including search engines, businesses gain real-time insights into market dynamics, consumer trends, and competitive intelligence. These data also have a high potential to inform research in the public interest.
Big Data Analytics using available information provided by various search engines (for instance, Google) has opened a new golden opportunity [1,2]. In addition, such an approach can also be used to measure people’s engagement, priority, and sentiment over time. For example, Google Trends [3] allows users to analyze the popularity of keywords and phrases on Google over time. It can be used for a variety of purposes, including analyzing public opinion, tracking the spread of information and news, and identifying trends in consumer behavior. Here are a few examples of studies that have used Google Trends data. For instance, Google Trends data has been used to predict the GDP growth in the United States [4]; Google Trends data was utilized to analyze tourism demand in various countries [5]; ref. [6] used Google Trends data to predict stock market returns in China; and [7] used Google Trends data to predict the spread of infectious diseases. These are just a few examples; there are many more studies and articles that have used Google Trends data in various ways (see, for example, [8,9,10,11,12,13,14,15,16,17]).
The landscape of online networks and interaction mediums has been significantly transformed by the advent of social media platforms. Unlike their predecessors, social media platforms exhibit distinctive characteristics such as openness, participatory dynamics, flexibility, robustness, and creativity. These platforms, akin to real-life social networks, establish virtual connections between individuals, giving rise to the small-world phenomena that characterize them (see, for instance, [18,19,20,21,22,23,24,25]. Recent statistics reveal that the average social media user maintains 8.4 active accounts and dedicates around 145 min daily to engaging across various social media platforms. Amidst this vibrant virtual environment, numerous challenges have emerged concerning the extraction and analysis of content generation, modification, and dissemination across diverse topics on social media. This paper draws on a recent review [26] and various other sources [27,28,29,30,31,32,33] to delve into the multifaceted challenges inherent in social media mining and analysis, shedding light on the complexities of understanding and navigating this dynamic landscape.
The novelty of this paper lies in its innovative approach to social trend mining, which leverages open access data and Google Search engine data to provide comprehensive insights into user behavior, and trend identification. By integrating time series analysis and advanced analytical methods, this paper offers a fresh perspective on understanding and harnessing social data for decision making. Furthermore, this paper introduces the novel application of wavelet coherence analysis and phase difference to uncover hidden relationships and patterns within social data, enhancing our ability to identify leading and lagging trends. This unique combination of methodologies and its adaptability to various domains make the paper a pioneering contribution to the field of social trend mining.
The ability to identify leading and lagging trends, perform trend analysis, and differentiate noise from meaningful events provides organizations with a powerful tool for informed decision making, proactive strategy formulation, and effective risk management.
The next section presents the methodology, which consists of two subsections: trend extraction based on indexed time series and coherence analysis between two time series. These methodologies are employed to explore the applicability of the proposed approach using real data extracted from Google Search.
The first subsection focuses on trend extraction based on indexed time series. By utilizing indexed time series data, trends can be identified and analyzed. This approach allows for the examination of temporal patterns and variations in search interest over time. The indexed time series data, obtained from Google Search data, serve as a valuable resource for trend analysis and provide insights into societal interests and preferences.
The second subsection delves into coherence analysis between two time series. This analysis explores the association between two time series, considering both time and frequencies. By examining the coherence between the two series, it becomes possible to understand the degree of similarity and correlation between them. This analysis provides valuable information about the interconnectedness and potential causal relationships between different social phenomena.
To evaluate the proposed approach, real data extracted from Google searches are utilized. These data represent actual search queries made by individuals, reflecting their interests and concerns. By employing this real-world data, the applicability and effectiveness of the methodology can be assessed, providing valuable insights into social intelligence mining.

2. Methodology and Methods

The methodology section of this paper briefly describes a trend extraction approach based on indexed time series and an approach to coherence analysis between two time series. By utilizing real data from Google Search, the proposed approach can be evaluated and its effectiveness in uncovering trends and associations can be assessed. These methodologies serve as powerful tools for exploring and analyzing social phenomena, offering valuable insights into the dynamics of our ever-evolving society.

2.1. Trend Analysis

Search engine trend analysis has emerged as a valuable technique for gaining insights into user behavior, sentiment analysis, and trend identification. Among various search engines, Google Trends stands out as a prominent platform that provides a vast amount of data, which can be utilized for time series analysis and opinion mining of individuals.
By monitoring the frequency of searches related to specific keywords over time, researchers can obtain valuable insights into the popularity and dynamics of various topics. This data can be leveraged for time series analysis techniques, such as trend identification, seasonality detection, and forecasting. The data extracted from Google Trends data provides not only quantitative information but also a glimpse into public sentiment and opinions. By analyzing the content of search queries and associated search results, researchers can gain insights into the preferences, concerns, and sentiments of individuals. Opinion mining techniques, such as sentiment analysis and topic modeling, can be applied to extract meaningful insights from this data, enabling a deeper understanding of public opinion on specific subjects.
To facilitate trend analysis and comparison across different topics, the concept of creating indices based on selected keywords can be used. These indices capture the relative popularity or interest in specific topics over time. The formula for creating indices can be adjusted based on the desired characteristics and data normalization techniques. Two common types of indices are:
(a) Univariate Google Trend Index: This index represents the search interest for a single keyword or topic. It is calculated by normalizing the search volume or frequency for the chosen keyword over a specific time period. Normalization techniques, such as dividing by the maximum search volume or using z-scores, can be employed to standardize the data and make it comparable.
(b) Multivariate Google Trend Index: This index captures the comparative popularity of multiple keywords or topics. It involves selecting a set of keywords of interest and calculating the search volume or frequency for each keyword.
By utilizing these indices, researchers and practitioners can gain a comprehensive view of the relative popularity and trends associated with different keywords or topics over time. This enables them to identify emerging trends, track public sentiment, and compare the performance of various keywords or topics within their respective domains.

2.2. Lead and Lag Analysis

A wavelet transform is used to transform time series with complex periodic behavior to simplified signals, each of which has simple periodic behavior (with a single period). From a mathematical point of view, a wavelet transform is a generalization of Fourier transform. A Continuous Wavelet Transform, CWT, uses a mother wavelet function ψ ( ) to transform a discrete-time time series { y t } 1 n to wavelet coefficients W ψ { y } ( τ , s ) , for the time localizing parameter τ and the scale parameter s .

2.2.1. Univariate Case

The wavelet coefficients W ψ { y } ( τ , s ) are defined as a convolution of time series { y t } 1 n with the localized mother wavelet ψ ( ) (named child wavelet), localized in time and frequency space by τ and s [34]:
W ψ { y } ( τ , s ) = t = 1 n y t   1 s   ψ ¯ ( t τ s ) ,
where ψ ¯ ( ) is the complex conjugate of the mother wavelet ψ ( ) . The localization parameter τ exhibits periodic behavior over time, while the scale parameter s localizes the periodic behavior in the frequency domain. When the scale parameter s has larger values, this indicates long-term periodic behavior with low frequency. On the other hand, smaller values of the scale parameter s reveal details in short-term periodic patterns with higher frequencies. One commonly used choice for the mother wavelet is the Morlet wavelet [33], which is formulated as follows:
ψ ( t ) = c ω π 1 4 exp { t 2 2 } ( e i ω t κ ω ) ,
where ω is the angular frequency, and κ ω and c ω are constants defined as:
c ω = ( 1 + e ω 2 2 e 3 4 ω 2 ) 1 2 , κ ω = e 1 2 ω 2 .
The ω = 6 is a proper choice for the angular frequency since it makes the Morlet wavelet approximately analytic. Large absolute values of W ψ { y } ( τ , s ) indicate powerful periodic patterns in time τ and period s . The wavelet coefficients can be used to construct the wavelet power spectrum of time series { y t } 1 n :
P o w e r ψ { y } ( τ , s ) = 1 s | W ψ { y } ( τ , s ) | 2
The wavelet power spectrum, denoted as P o w e r ψ { y } , is a valuable tool for mapping periodic patterns in a given time series over time. To assess the significance of the wavelet power spectrum, it can be compared against the white noise spectrum using either the asymptotic chi-square statistic [34] or Monte Carlo simulation [35]. The Monte Carlo simulation approach is employed for evaluating the significance of the wavelet power spectrum.

2.2.2. Bivariate Case

Let us now consider the time series { x t } 1 n and { y t } 1 n as the bivariate case. A cross-wavelet transform can be used to investigate the relationship between { x t } 1 n and { y t } 1 n [34]:
W ψ { x y } ( τ , s ) = 1 s W ψ { x } ( τ , s )   W ¯ ψ { y } ( τ , s ) ,
where W ¯ denotes a complex conjugate and W ψ { x } ( τ , s ) and W ψ { y } ( τ , s ) are the wavelet coefficients in CWT of { x t } 1 n and { y t } 1 n , respectively. The wavelet cross power spectrum, as modulus of wavelet coefficients, can be used to map the similarities between two time series’ periodic behavior:
P o w e r ψ { x y } ( τ , s ) = | W ψ { x y } ( τ , s ) | .
The P o w e r ψ { x y } ( τ , s ) , like covariance, depends on the underlying time series’ unit of measurement and may not properly interpret the degree of association between two series. Wavelet coherence between two time series { x t } 1 n and { y t } 1 n is defined as the local cross-correlation between the series, localized at time τ and scale s :
W ψ { x y } ( τ , s ) = | s W ψ { x y } ( τ , s ) | 2 s P o w e r ψ { x } ( τ , s ) . s P o w e r ψ { y } ( τ , s ) ,
where prefix s behind W ψ and P o w e r ψ indicates smoothing is required. Similar to the power spectrum, the wavelet coherence between two series can also be examined using Monte Carlo simulation [36,37,38,39]. Monte Carlo simulation provides a robust approach for testing the significance of wavelet coherence and assessing the presence of coherent relationships between the two series under investigation.
The Continuous Wavelet Transform (CWT) reveals localized periodic patterns in a given time series { y t } 1 n . The wavelet phase indicates the local displacement of the periodic behavior relative to the localization parameter τ, which is shifted across the time domain when τ is set as the origin. The wavelet phase is typically represented as an angle within the interval [−π, π].
P h a s e ψ { y } ( τ , s ) = tan 1 ( I m ( W ψ { y } ( τ , s ) ) R e ( W ψ { y } ( τ , s ) ) ) ,
where I m ( . ) and R e ( . ) are imaginary and real parts of the wavelet coefficient W ψ { y } ( τ , s ) .
Using the cross-wavelet coefficients, one can calculate the difference between wavelet phases from two time series (which is actually the difference between two phases):
A n g l e ψ { x y } ( τ , s ) = tan 1 ( I m ( W ψ { x y } ( τ , s ) ) R e ( W ψ { x y } ( τ , s ) ) ) = P h a s e ψ { x } ( τ , s ) P h a s e ψ { y } ( τ , s ) ,
where A n g l e ψ { x y } ( τ , s ) represents the phase difference between two time series { x t } 1 n and { y t } 1 n . A n g l e ψ { x y } ( τ , s ) can be used to determine which time series starts the periodic pattern first and which one is following, for a given time and frequency interval. Figure 1 shows the simplified interpretation of the phase difference between time series { x t } 1 n and { y t } 1 n .
Various social phenomena can exhibit cyclical patterns [33]. By applying wavelet analysis to the social data, we can uncover the underlying periodic patterns in these phenomena. For example, analyzing people’s interest in a subject through chat discussions or online searches can reveal if there are cycles where this subject becomes popular in society. Similarly, examining the number of participants in a social activity can expose the cycles of outbursts in that particular activity.
Furthermore, by utilizing a wavelet coherence analysis and phase difference, we can examine the correlation between two social phenomena, even if it was only for a brief period. This analysis can help determine which phenomenon had a leading role if such a relationship existed. The presence of coherency and phase difference between two social phenomena could indicate a causal relationship, where events from the leading series influence events in the lagging series. Alternatively, it could suggest that both series are influenced by another common social phenomenon. These findings serve as powerful tools for generating hypotheses about social events, trends, and their interrelations.

3. Applied Methodology: Real Data Implementation and Analysis

In this section, we focus on the application of social trend mining to several dimensions of human security including food, water, and energy security. These three dimensions of human security correspond to three UN Sustainable Development Goals (SDGs)—SDG 2, SDG 6, and SDG 7, respectively. Let us now provide a brief overview of three SDGs utilized in this paper.
Sustainable Development Goal 2 is about creating a world free of hunger by 2030. In 2020, between 720 million and 811 million persons worldwide were suffering from hunger, roughly 161 million more than in 2019. Also in 2020, a staggering 2.4 billion people, or above 30 percent of the world’s population, were moderately or severely food insecure, lacking regular access to adequate food. The figure increased by nearly 320 million people in just one year. Globally, 149.2 million children under 5 years of age, or 22.0 percent, were suffering from stunting (low height for their age) in 2020, a decrease from 24.4 percent in 2015.
SDG 6 is about ensuring access to water and sanitization for all. Access to safe water, sanitation, and hygiene is the most basic human need for health and well-being. Billions of people will lack access to these basic services in 2030 unless progress quadruples. Demand for water is rising owing to rapid population growth, urbanization, and increasing water needs from the agriculture, industry, and energy sectors.
To reach universal access to drinking water, sanitation, and hygiene by 2030, the current rates of progress would need to increase fourfold. Achieving these targets would save 829,000 people annually, who die from diseases directly attributable to unsafe water, inadequate sanitation, and poor hygiene practices.
SDG 7 is about ensuring access to clean and affordable energy, which is key to the development of agriculture, business, communications, education, healthcare, and transportation. The lack of access to energy hinders economic and human development.
The latest data suggest that the world continues to advance towards sustainable energy targets. Nevertheless, the current pace of progress is insufficient to achieve Goal 7 by 2030. Huge disparities in access to modern sustainable energy persist.
It should be mentioned that significant challenges remain at the global level in terms of achieving these SDGs. This assessment is true for most of the world’s major regions, while recent trends are mainly stagnating (in lower-income countries) or moderately increasing (in higher-income countries).
The actual or perceived lack of food, water, or energy security could be a source of social instability. In addition to the indicators describing the actual availability, accessibility, and affordability of food, water, and energy, such as prices and use, indicators describing people’s perceptions provide important input for policy makers. Google Search data can inform such indicators which could be made available to policy makers almost in real time.
Figure 2, as an example, depicts the Google Search hits for the keywords “Food Security”, “Energy Security”, and “Water Security” over the past five years. These three concepts—food, energy, and water security—are important dimensions of human security. However, when comparing the search hits for the three subsets, it is evident that the search interest in food security is significantly higher than that for water security and energy security. The search interest in energy security is lower than the other two dimensions although only slightly lower than the search interest in water security.
Let us now explore the periodic behavior in the search interests for “Food Security” and “Water Security”. Figure 3 displays the wavelet power spectrum for both series.
As depicted in Figure 3 (top panel), the interest in “Food Security” has exhibited significant low and mid-frequency behaviors over the past five years. Note that low-frequency cycles are cycles with long periods, for example, longer than 32 weeks period in this data.
Mid-frequency cycles are cycles with mid-range periods, for example, around a 16-week period in this data. The significance test, conducted using Monte Carlo simulation with 5000 sample paths, confirms this observation. However, in recent years (i.e., after 19-Apr-2020), the power spectrum has shown an increase, with a focus on mid-range periods (around 16 and 32 weeks) and long-range periods (above 32 weeks). This indicates that the search frequency for the keyword “Food Security” has become more frequent over time.
In the middle panel of Figure 3, the power spectrum for “Water Security” displays significant patterns mostly concentrated on the right side of the timeline (approximately after 15-Nov-2020). The most powerful periodic patterns occur within the 16 to 32-week period range. In other words, approximately after 15-Nov-2020, there has been a periodic pattern in people’s interest in the “Water Security” keyword. The length of each periodic pattern (the beginning of one surge of interest to the beginning of the next one) mostly includes periods below 16 to periods above 32 weeks.
As shown in the bottom panel, the significant “Energy Security” power spectrum also is mostly concentrated on low and mid frequencies (i.e., long periods in which the time from one surge of interest in “Energy Security” is between 32 to 64 weeks and mid-ranged periods which the time from one surge of interest in “Energy Security” is between 16 to 32 weeks). Periodic behavior of interest in the “Energy Security” keyword has become more powerful after 13-Jun-2021. Furthermore, the periodic behavior of interest in “Energy Security” includes shorter periods (higher frequency) as well. In other words, the power spectrum of interest in “Energy Security” shows that the interest in “Energy Security” has increased and the search for “Energy Security” has become more frequent.
These findings suggest that the interest in “Food Security”, “Water Security”, and “Energy Security” has increased in the last three years (after 14-Apr-2020), and searching for these keywords has become more frequent. Furthermore, it can be seen the increased interest in these keywords started with “Food Security” and, as the top panel shows an increased power spectrum at higher frequencies sooner. In other words, after 14-Apr-2020, the search for “Food Security” became more frequent, then the search for “Water Security”, and, finally, “Energy Security”.
Additionally, in recent years (especially after 13-Jun-2021) the periodic behavior of interest in these keywords demonstrates mid-range periodic patterns (with period lengths between 16 and 32 weeks.), which suggests that it takes 16 to 32 weeks (almost 4 to 8 months) form one surge of interest in these keywords to the next one.
In order to examine the relationship between interest in “Food Security” and “Water Security”, the wavelet coherence analysis is applied to two series. The results are given in Figure 4.
As previously mentioned, wavelet coherence and phase difference analysis are valuable tools for examining the relationship between two time series signals, such as food security and water security, in both the time and frequency domains. These techniques provide insights into the similarity, coherence, and phase relationship between the two signals at different scales or frequencies, over time.
Wavelet coherence quantifies the correlation between the two signals as a function of both time and frequency. It reveals the level of similarity or shared variability between the two time series across different frequency components. Higher coherence values indicate a stronger relationship, while lower coherence values suggest a weaker or non-existent relationship.
On the other hand, phase difference captures the phase lag or lead between the two signals. It indicates the relative timing or synchronization between the peaks and troughs of the two time series. A phase difference of zero denotes perfect synchronization, implying that the peaks and troughs of the two signals align precisely. Non-zero phase difference values indicate a lag or lead between the two signals, with the peaks and troughs occurring at different times.
By employing wavelet coherence and phase difference analysis, we can gain a deeper understanding of the relationship between food security and water security, uncovering their coherence and phase synchronization characteristics across different frequencies and over time.
In order to analyze the coherence between each two of these three time series, only the time intervals and periods in which the coherence of the two series is significant, and their power spectrum will be considered. Furthermore, in order to avoid overestimating the number of cyclical patterns, the results are presented for time intervals and periods whose length is not shorter than half of their period and at least 5 weeks apart.
The coherence analysis between “Food Security” and “Water Security” (top panel in Figure 4) reveals that the two series exhibit significant coherence mostly in mid-range periods, i.e., 15.45-week and 25.11-week periods, and long periods, i.e., 32-week, 38.1-week, 43.71-week, and 64-week periods (see Table A1 in Appendix A for more details). In other words, significant cyclical patterns are evident in both series and demonstrate a significant coherence between them, suggesting that they behave similarly, possibly with a time delay. It also can be seen that during the time, the frequencies in which two series have become coherent are increased (the length of periods is decreased). For instance, before the end of 2020, coherence between two series existed mostly at 43.71-week and 64-week periods ( 1 43.71 = 0.02287806 and 1 64 = 0.015625 frequencies, respectively) while after June 2022, the coherence between the two series has occurred mostly in 25.11-week and 15.45-week periods ( 1 15.45 = 0.06472492 and 1 25.11 = 0.03982477 frequencies, respectively).
The middle panel in Figure 4 shows significant coherency between “Food Security” and “Energy Security”. Coupling these results with the power spectrum results (Figure 3) reveals that the two series exhibit significant coherence mostly in mid-range periods, i.e., 18.38-week and 25.11-week periods, and longer periods, i.e., 34.3-week and 39.4-week periods (see Table A2 for more details). This means there are significant cyclical patterns in both series with these periods in which they behave similarly in a time interval, possibly with a time delay. For instance, between 13-Feb-2022 and 3-Jul-2022, both series demonstrate a significant cyclical pattern in which it would take 25.11 weeks from one surge of interest in keywords to the next one, and two series have almost the same behavior possibly with a time delay.
Significant coherence between “Water Security” and “Energy Security” is presented in the bottom panel of Figure 4. Overlapping significant coherence areas (inside white contours) with “Water Security” and “Energy Security” power spectrums (Figure 3) shows that two series have significant coherence, mostly in mid-range periods (i.e., 17.15-week and 25.11-week periods) and very long period, i.e., 84.45-week period (see Table A3 for details).
The arrows displayed in above mentioned periods indicate the angular phase difference between the two series during each period. To convert these angular phase differences into a time unit (weeks), the following formulation can be utilized.
P h a s e . d i f f ψ { x y } ( τ , s ) = l p 2 π A n g l e ψ { x y } ( τ , s ) ,
where P h a s e . d i f f ψ { x y } ( τ , s ) is the phase difference between two series, measured by time unit (which is “week” in our data), l p is the period length and A n g l e ψ { x y } ( τ , s ) is angular phase difference measured in radians. For instance, in weekly data, the angular phase difference between two series for a 25.11-week period converts to phase difference in weeks as:
P h a s e . d i f f ψ { x y } ( τ , s ) = 25.11 2 π A n g l e ψ { x y } ( τ , s )
Table A1, Table A2 and Table A3 in Appendix A illustrate the phase difference and leading time series in each of the time intervals and periods discussed above. According to Table A1, Table A2 and Table A3, all three time series have significant cyclical behavior with a 25.11-week period at some point over the time and each time series has significant coherence with another one. For instance, between 15-Apr-2022 and 3-Jul-2022, there is significant coherence between all pairs of time series, i.e., in the “Food Security”–”Water Security” pair, in the “Food Security”–”Energy Security” pair, and in “Water Security–Energy Security”. In other words, in this short time interval, all three time series have a cyclical pattern in which the surge of interest in any one keyword to the next surge of interest in that keyword takes almost 25.11 weeks. Furthermore, the cyclical pattern is similar in all three time series, except for possible time delay. However, the cyclical pattern and coherence for each pair of time series may exceed this time interval differently. Phase difference analysis shows that between 15-Apr-2022 and 3-Jul-2022, in a cyclical pattern with a 25.11-week period, interest in “Food Security” leads both “Water Security” and “Energy Security” (with different phase difference values) and interest in “Energy Security” leads interest in “Water Security”. These results imply that there is a cyclical pattern with a 25.11-week period length, between 15-Apr-2022 and 3-Jul-2022, in which the surge of interest in keywords first comes to “Food Security” and then “Energy Security” and, finally, “Water Security”.

4. Search Engine: Comparative Analysis

Table 1 presents a comparative analysis of the trend extraction features offered by three prominent search engines: Google, Bing, and Yahoo. It serves as a valuable reference for users and decision makers seeking to understand the capabilities of these search engines in extracting and analyzing trending data.
In the table, various key features are assessed, including data accessibility, trend analysis tools, real-time data availability, geographic specificity, data visualization options, API support, and customization capabilities.
Google stands out with its extensive data accessibility, strong trend analysis tools, and real-time data availability, making it a robust choice for users interested in tracking and analyzing trends. Bing offers decent trend analysis capabilities and some customization options, making it a suitable alternative. Yahoo, on the other hand, offers limited data accessibility and trend analysis tools, making it less suited for in-depth trend extraction and analysis tasks.
Overall, this comparative analysis provides insights into the strengths and weaknesses of these search engines concerning trend extraction, enabling users to make informed choices based on their specific data analysis needs and preferences.

5. Discussion

This paper delves into the concept of Social Intelligence Mining, highlighting the importance of leveraging open access data and Google Search engine data for trend analysis. This approach offers several notable advantages for both researchers and practitioners.
Firstly, the analysis of Google Search data as a time series provides a powerful tool for trend identification. By examining search queries over time, researchers can pinpoint emerging trends, recognize seasonality patterns, and even make predictions about future developments. This temporal perspective is invaluable for staying ahead in rapidly evolving fields and industries.
Furthermore, the application of opinion mining techniques to search queries offers a more profound understanding of public sentiment and preferences. This sentiment analysis adds a layer of nuance to the data, enabling decision makers to make informed choices regarding strategy formulation and risk management. In sum, Social Intelligence Mining, driven by open access data and Google Search engine data, equips organizations with comprehensive insights into user behavior, sentiment analysis, and trend identification.
This approach facilitates competitive advantages, informed decision making, and meaningful engagement with target audiences. As technology and data continue to evolve, the potential of Social Intelligence Mining for shaping strategies, driving innovation, and creating value across diverse domains remains substantial.
Additionally, the paper highlights the utility of wavelet coherence analysis and phase difference in uncovering hidden relationships and patterns within social data. These techniques offer a more profound understanding of the dynamics between social phenomena. By identifying leading and lagging trends, researchers and practitioners can make well-informed decisions based on a more comprehensive view of the data.
The specific case study presented in the paper on “Food Security”, “Energy Security”, and “Water Security” serves as an illustrative example. However, the methodology outlined can be applied to a wide range of domains and topics. Open access data sources like Google Trends offer valuable insights into public sentiment, emerging trends, and proactive strategy development.
Looking ahead, further research in this field should consider expanding the analysis to encompass additional relevant social phenomena. This expansion will allow for the exploration of more complex relationships and patterns. Additionally, incorporating data from social media and news sources can provide a more comprehensive understanding of social dynamics.
Addressing the challenges associated with data quality, privacy, and bias is also crucial for ensuring the reliability and validity of results in Social Intelligence Mining. As this field continues to evolve, these challenges must be carefully addressed to maintain the integrity of the research and its practical applications.

6. Conclusions

In conclusion, this paper underscores the pivotal role of Social Intelligence Mining, accentuating the utility of open access data and Google Search engine data for in-depth trend analysis. Harnessing these resources, coupled with sophisticated analytical methods, empowers organizations to secure a competitive advantage, make evidence-based decisions, and more effectively engage their target demographics.
Our findings spotlight the efficacy of wavelet coherence analysis and phase difference in elucidating concealed relationships and patterns within social datasets. Such techniques facilitate a more profound grasp of social phenomena dynamics, subsequently refining decision-making protocols.
However, this research is not without its limitations. The focus on a singular case study, albeit comprehensive, may not capture the entire spectrum of possibilities within Social Intelligence Mining. Looking forward, the versatility of the presented methodology suggests its applicability across diverse domains and subjects. Still, future research should venture into investigating intricate relationships and broaden its analytical scope to encapsulate various social phenomena. Integrating data from diverse platforms, such as social media and news outlets, will enrich the analysis. Addressing pressing concerns of data integrity, privacy, and potential biases will be paramount to buttress the dependability of subsequent Social Intelligence Mining endeavors.
To encapsulate, Social Intelligence Mining stands poised to redefine strategy formulation, spur innovation, and offer unparalleled value across sectors. Its sustained evolution augurs well for refining both decision-making paradigms and the comprehension of intricate social dynamics.

Author Contributions

H.H., N.K., E.R. and M.R.Y. conceptualied and designed the study and methodology. H.H. and M.R.Y. developed the software code and conducted formal data analysis. H.H. prepared the original draft. E.R., M.R.Y. and N.K. reviewed and edited the paper. All authors have read and agreed to the published version of the manuscript.


IIASA internal funding.

Data Availability Statement

Data are available upon request.


We extend our sincere gratitude to the referees and the editor, whose insightful comments and suggestions significantly contributed to the enhancement of our paper.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. The phase difference and leading time series for “Food Security”–” Water Security” pair.
Table A1. The phase difference and leading time series for “Food Security”–” Water Security” pair.
DateCoherencePhase Diff. (Angular)Phase Diff. (Temporal)Leading SeriesDateCoherencePhase Diff. (Angular)Phase Diff. (Temporal)Leading Series
15.45-week period
23-Jan-220.99580.03430.0844Food Security17-Apr-220.99660.27590.6786Food Security
30-Jan-220.99630.04680.1151Food Security24-Apr-220.99660.30230.7436Food Security
6-Feb-220.99660.06070.1493Food Security1-May-220.99660.32850.808Food Security
13-Feb-220.99690.07610.1872Food Security8-May-220.99670.35430.8715Food Security
20-Feb-220.9970.0930.2288Food Security15-May-220.99680.37930.933Food Security
27-Feb-220.99710.11150.2743Food Security22-May-220.99690.40320.9918Food Security
6-Mar-220.99710.13140.3232Food Security29-May-220.99710.42551.0466Food Security
13-Mar-220.9970.15280.3758Food Security5-Jun-220.99730.44581.0966Food Security
20-Mar-220.9970.17550.4317Food Security12-Jun-220.99740.46391.1411Food Security
27-Mar-220.99690.19940.4905Food Security19-Jun-220.99750.47931.179Food Security
3-Apr-220.99680.22420.5515Food Security26-Jun-220.99750.49161.2092Food Security
10-Apr-220.99670.24980.6144Food Security3-Jul-220.99740.50041.2309Food Security
25.11-week period
15-May-220.9966−1.6031−6.4058Food Security26-Jun-220.9993−1.6985−6.787Food Security
22-May-220.9971−1.6216−6.4797Food Security3-Jul-220.9994−1.7098−6.8321Food Security
29-May-220.9977−1.6392−6.55Food Security10-Jul-220.9993−1.7194−6.8705Food Security
5-Jun-220.9981−1.6558−6.6163Food Security17-Jul-220.999−1.7269−6.9004Food Security
12-Jun-220.9986−1.6713−6.6783Food Security24-Jul-220.9983−1.7322−6.9216Food Security
19-Jun-220.999−1.6856−6.7354Food Security31-Jul-220.9972−1.7347−6.9316Food Security
32-week period
24-Oct-210.9963−1.0047−5.1169Water Security19-Dec-210.9974−1.0521−5.3583Water Security
31-Oct-210.9966−1.0097−5.1424Water Security26-Dec-210.9973−1.059−5.3934Water Security
7-Nov-210.9968−1.015−5.1694Water Security2-Jan-220.9973−1.0661−5.4296Water Security
14-Nov-210.9971−1.0206−5.1979Water Security9-Jan-220.9972−1.0733−5.4663Water Security
21-Nov-210.9972−1.0265−5.2279Water Security16-Jan-220.997−1.0806−5.5035Water Security
28-Nov-210.9973−1.0326−5.259Water Security23-Jan-220.9968−1.0881−5.5416Water Security
5-Dec-210.9974−1.0389−5.2911Water Security30-Jan-220.9966−1.0957−5.5804Water Security
12-Dec-210.9974−1.0454−5.3242Water Security6-Feb-220.9964−1.1034−5.6196Water Security
38.1-week period
11-Apr-210.9963−1.2271−7.432Water Security17-Oct-210.9979−1.0351−6.2692Water Security
18-Apr-210.9965−1.2208−7.3939Water Security24-Oct-210.9979−1.0271−6.2207Water Security
25-Apr-210.9966−1.2145−7.3557Water Security31-Oct-210.9979−1.0191−6.1723Water Security
2-May-210.9967−1.2081−7.317Water Security7-Nov-210.9979−1.0109−6.1226Water Security
9-May-210.9969−1.2017−7.2782Water Security14-Nov-210.9979−1.0027−6.0729Water Security
16-May-210.997−1.1952−7.2388Water Security21-Nov-210.9978−0.9945−6.0233Water Security
23-May-210.9971−1.1886−7.1989Water Security28-Nov-210.9978−0.9862−5.973Water Security
30-May-210.9972−1.182−7.1589Water Security5-Dec-210.9978−0.9778−5.9221Water Security
6-Jun-210.9972−1.1753−7.1183Water Security12-Dec-210.9978−0.9693−5.8706Water Security
13-Jun-210.9973−1.1685−7.0771Water Security19-Dec-210.9977−0.9608−5.8192Water Security
20-Jun-210.9974−1.1617−7.0359Water Security26-Dec-210.9977−0.9521−5.7665Water Security
27-Jun-210.9975−1.1547−6.9935Water Security2-Jan-220.9976−0.9435−5.7144Water Security
4-Jul-210.9975−1.1477−6.9511Water Security9-Jan-220.9976−0.9347−5.6611Water Security
11-Jul-210.9976−1.1407−6.9087Water Security16-Jan-220.9975−0.9259−5.6078Water Security
18-Jul-210.9976−1.1336−6.8657Water Security23-Jan-220.9975−0.917−5.5539Water Security
25-Jul-210.9977−1.1264−6.8221Water Security30-Jan-220.9974−0.908−5.4994Water Security
1-Aug-210.9977−1.1191−6.7779Water Security6-Feb-220.9973−0.8989−5.4443Water Security
8-Aug-210.9978−1.1118−6.7337Water Security13-Feb-220.9973−0.8897−5.3885Water Security
15-Aug-210.9978−1.1044−6.6889Water Security20-Feb-220.9972−0.8805−5.3328Water Security
22-Aug-210.9978−1.097−6.6441Water Security27-Feb-220.9971−0.8712−5.2765Water Security
29-Aug-210.9978−1.0895−6.5986Water Security6-Mar-220.9971−0.8618−5.2196Water Security
5-Sep-210.9979−1.0819−6.5526Water Security13-Mar-220.997−0.8523−5.162Water Security
12-Sep-210.9979−1.0742−6.506Water Security20-Mar-220.9969−0.8427−5.1039Water Security
19-Sep-210.9979−1.0665−6.4593Water Security27-Mar-220.9968−0.833−5.0451Water Security
26-Sep-210.9979−1.0588−6.4127Water Security3-Apr-220.9967−0.8232−4.9858Water Security
3-Oct-210.9979−1.051−6.3655Water Security10-Apr-220.9967−0.8134−4.9264Water Security
10-Oct-210.9979−1.0431−6.3176Water Security
43.71-week period
4-Oct-200.996−0.8364−5.819Water Security23-May-210.9998−0.9986−6.9474Water Security
11-Oct-200.9961−0.8437−5.8698Water Security30-May-210.9998−0.9982−6.9447Water Security
18-Oct-200.9962−0.8512−5.922Water Security6-Jun-210.9997−0.9975−6.9398Water Security
25-Oct-200.9963−0.8587−5.9741Water Security13-Jun-210.9997−0.9964−6.9321Water Security
1-Nov-200.9964−0.8662−6.0263Water Security20-Jun-210.9996−0.995−6.9224Water Security
8-Nov-200.9965−0.8738−6.0792Water Security27-Jun-210.9996−0.9932−6.9099Water Security
15-Nov-200.9967−0.8813−6.1314Water Security4-Jul-210.9995−0.9911−6.8953Water Security
22-Nov-200.9969−0.8887−6.1829Water Security11-Jul-210.9994−0.9885−6.8772Water Security
29-Nov-200.997−0.8961−6.2343Water Security18-Jul-210.9993−0.9857−6.8577Water Security
6-Dec-200.9972−0.9033−6.2844Water Security25-Jul-210.9993−0.9825−6.8354Water Security
13-Dec-200.9974−0.9104−6.3338Water Security1-Aug-210.9992−0.9789−6.8104Water Security
20-Dec-200.9976−0.9174−6.3825Water Security8-Aug-210.9991−0.975−6.7833Water Security
27-Dec-200.9978−0.9241−6.4291Water Security15-Aug-210.9989−0.9708−6.754Water Security
3-Jan-210.9979−0.9307−6.4751Water Security22-Aug-210.9988−0.9662−6.722Water Security
10-Jan-210.9981−0.937−6.5189Water Security29-Aug-210.9987−0.9613−6.6879Water Security
17-Jan-210.9983−0.9431−6.5613Water Security5-Sep-210.9986−0.9561−6.6518Water Security
24-Jan-210.9985−0.949−6.6024Water Security12-Sep-210.9985−0.9506−6.6135Water Security
31-Jan-210.9986−0.9545−6.6406Water Security19-Sep-210.9983−0.9448−6.5731Water Security
7-Feb-210.9988−0.9598−6.6775Water Security26-Sep-210.9982−0.9386−6.53Water Security
14-Feb-210.9989−0.9648−6.7123Water Security3-Oct-210.9981−0.9322−6.4855Water Security
21-Feb-210.9991−0.9695−6.745Water Security10-Oct-210.9979−0.9255−6.4389Water Security
28-Feb-210.9992−0.9738−6.7749Water Security17-Oct-210.9978−0.9185−6.3902Water Security
7-Mar-210.9993−0.9778−6.8027Water Security24-Oct-210.9976−0.9113−6.3401Water Security
14-Mar-210.9994−0.9815−6.8285Water Security31-Oct-210.9975−0.9038−6.2879Water Security
21-Mar-210.9995−0.9849−6.8521Water Security7-Nov-210.9974−0.896−6.2336Water Security
28-Mar-210.9996−0.9879−6.873Water Security14-Nov-210.9972−0.8879−6.1773Water Security
4-Apr-210.9997−0.9905−6.8911Water Security21-Nov-210.9971−0.8796−6.1195Water Security
11-Apr-210.9997−0.9927−6.9064Water Security28-Nov-210.9969−0.8711−6.0604Water Security
18-Apr-210.9998−0.9946−6.9196Water Security5-Dec-210.9968−0.8624−5.9999Water Security
25-Apr-210.9998−0.9962−6.9307Water Security12-Dec-210.9966−0.8534−5.9373Water Security
2-May-210.9998−0.9973−6.9384Water Security19-Dec-210.9965−0.8442−5.8733Water Security
9-May-210.9998−0.9981−6.944Water Security26-Dec-210.9963−0.8348−5.8079Water Security
16-May-210.9998−0.9985−6.9468Water Security2-Jan-220.9962−0.8253−5.7418Water Security
64-week period
16-Dec-180.99832.607626.5608Water Security2-Jun-190.9982.597526.4579Water Security
23-Dec-180.99832.60726.5547Water Security9-Jun-190.9982.597526.4579Water Security
30-Dec-180.99832.606526.5496Water Security16-Jun-190.9982.597526.4579Water Security
6-Jan-190.99832.605926.5435Water Security23-Jun-190.9982.597626.4589Water Security
13-Jan-190.99832.605426.5384Water Security30-Jun-190.99792.597826.461Water Security
20-Jan-190.99822.604826.5323Water Security7-Jul-190.99792.59826.463Water Security
27-Jan-190.99822.604326.5272Water Security14-Jul-190.99792.598326.4661Water Security
3-Feb-190.99822.603726.5211Water Security21-Jul-190.99792.598726.4701Water Security
10-Feb-190.99822.603226.516Water Security28-Jul-190.99792.599226.4752Water Security
17-Feb-190.99822.602726.5109Water Security4-Aug-190.99782.599726.4803Water Security
24-Feb-190.99822.602226.5058Water Security11-Aug-190.99782.600426.4875Water Security
3-Mar-190.99822.601726.5007Water Security18-Aug-190.99782.601126.4946Water Security
10-Mar-190.99822.601226.4956Water Security25-Aug-190.99772.60226.5038Water Security
17-Mar-190.99812.600726.4905Water Security1-Sep-190.99772.602926.5129Water Security
24-Mar-190.99812.600326.4864Water Security8-Sep-190.99772.60426.5241Water Security
31-Mar-190.99812.599826.4813Water Security15-Sep-190.99762.605226.5363Water Security
7-Apr-190.99812.599426.4773Water Security22-Sep-190.99762.606526.5496Water Security
14-Apr-190.99812.599126.4742Water Security29-Sep-190.99752.607926.5639Water Security
21-Apr-190.99812.598726.4701Water Security6-Oct-190.99752.609426.5791Water Security
28-Apr-190.99812.598426.4671Water Security13-Oct-190.99742.611126.5964Water Security
5-May-190.99812.598126.464Water Security20-Oct-190.99732.612926.6148Water Security
12-May-190.9982.597926.462Water Security27-Oct-190.99722.614926.6352Water Security
19-May-190.9982.597726.46Water Security3-Nov-190.99722.61726.6565Water Security
26-May-190.9982.597626.4589Water Security
Table A2. The phase difference and leading time series for “Food Security”–”Energy Security” pair.
Table A2. The phase difference and leading time series for “Food Security”–”Energy Security” pair.
DateCoherencePhase Diff. (Angular)Phase Diff. (Temporal)Leading SeriesDateCoherencePhase Diff. (Angular)Phase Diff. (Temporal)Leading Series
18.38-week period
9-Oct-220.99690.49421.4456Food Security20-Nov-220.99930.51931.519Food Security
16-Oct-220.9980.49761.4555Food Security27-Nov-220.9990.52431.5336Food Security
23-Oct-220.99880.50141.4667Food Security4-Dec-220.99860.52961.5492Food Security
30-Oct-220.99920.50551.4787Food Security11-Dec-220.99810.53511.5652Food Security
6-Nov-220.99940.50991.4915Food Security18-Dec-220.99760.54071.5816Food Security
13-Nov-220.99940.51451.505Food Security25-Dec-220.99710.54661.5989Food Security
25.11-week period
13-Feb-220.99660.35241.4081Food Security1-May-220.99960.19250.7692Food Security
20-Feb-220.99710.33291.3302Food Security8-May-220.99970.18380.7344Food Security
27-Feb-220.99760.31441.2563Food Security15-May-220.99970.17630.7045Food Security
6-Mar-220.99790.2971.1868Food Security22-May-220.99960.16980.6785Food Security
13-Mar-220.99830.28061.1212Food Security29-May-220.99940.16450.6573Food Security
20-Mar-220.99860.26521.0597Food Security5-Jun-220.99920.16050.6413Food Security
27-Mar-220.99880.25071.0018Food Security12-Jun-220.99890.15790.6309Food Security
3-Apr-220.99910.23710.9474Food Security19-Jun-220.99850.15670.6262Food Security
10-Apr-220.99930.22450.8971Food Security26-Jun-220.99790.15720.6281Food Security
17-Apr-220.99940.21290.8507Food Security3-Jul-220.99710.15940.6369Food Security
24-Apr-220.99950.20220.808Food Security
34.3-week period
19-May-190.99621.21556.6348Food Security1-Sep-190.99941.23736.7538Food Security
26-May-190.99661.2146.6266Food Security8-Sep-190.99941.24256.7822Food Security
2-Jun-190.99691.21296.6206Food Security15-Sep-190.99941.24826.8133Food Security
9-Jun-190.99721.21216.6162Food Security22-Sep-190.99941.25456.8477Food Security
16-Jun-190.99751.21186.6146Food Security29-Sep-190.99941.26146.8853Food Security
23-Jun-190.99781.21186.6146Food Security6-Oct-190.99931.26876.9252Food Security
30-Jun-190.99811.21236.6173Food Security13-Oct-190.99911.27676.9689Food Security
7-Jul-190.99831.21326.6222Food Security20-Oct-190.99891.28527.0153Food Security
14-Jul-190.99851.21466.6299Food Security27-Oct-190.99871.29437.0649Food Security
21-Jul-190.99871.21646.6397Food Security3-Nov-190.99851.3047.1179Food Security
28-Jul-190.99891.21876.6523Food Security10-Nov-190.99821.31437.1741Food Security
4-Aug-190.9991.22146.667Food Security17-Nov-190.99781.32527.2336Food Security
11-Aug-190.99921.22466.6845Food Security24-Nov-190.99741.33677.2964Food Security
18-Aug-190.99931.22836.7047Food Security1-Dec-190.99691.34887.3624Food Security
25-Aug-190.99941.23256.7276Food Security8-Dec-190.99641.36157.4317Food Security
39.4-week period
6-Feb-220.9967−0.2142−1.3431Energy Security24-Apr-220.9997−0.1654−1.0371Energy Security
13-Feb-220.9972−0.2095−1.3136Energy Security1-May-220.9997−0.1615−1.0126Energy Security
20-Feb-220.9978−0.2049−1.2848Energy Security8-May-220.9996−0.1577−0.9888Energy Security
27-Feb-220.9982−0.2002−1.2553Energy Security15-May-220.9995−0.1541−0.9662Energy Security
6-Mar-220.9986−0.1956−1.2264Energy Security22-May-220.9993−0.1506−0.9443Energy Security
13-Mar-220.9989−0.1911−1.1982Energy Security29-May-220.9991−0.1473−0.9236Energy Security
20-Mar-220.9992−0.1866−1.17Energy Security5-Jun-220.9988−0.1442−0.9042Energy Security
27-Mar-220.9994−0.1822−1.1424Energy Security12-Jun-220.9985−0.1413−0.886Energy Security
3-Apr-220.9995−0.1778−1.1148Energy Security19-Jun-220.9982−0.1386−0.869Energy Security
10-Apr-220.9997−0.1736−1.0885Energy Security26-Jun-220.9978−0.1361−0.8534Energy Security
17-Apr-220.9997−0.1694−1.0622Energy Security3-Jul-220.9974−0.1339−0.8396Energy Security
Table A3. The phase difference and leading time series for “Water Security”–”Energy Security” pair.
Table A3. The phase difference and leading time series for “Water Security”–”Energy Security” pair.
DateCoherencePhase Diff. (Angular)Phase Diff. (Temporal)Leading SeriesDateCoherencePhase Diff. (Angular)Phase Diff. (Temporal)Leading Series
17.14-week period
30-Oct-220.99680.6411.7494Water Security18-Dec-220.99950.7141.9487Water Security
6-Nov-220.9980.65431.7857Water Security25-Dec-220.99920.72011.9653Water Security
13-Nov-220.99880.66691.8201Water Security1-Jan-230.99880.72521.9793Water Security
20-Nov-220.99940.67861.8521Water Security8-Jan-230.99830.72951.991Water Security
27-Nov-220.99970.68911.8807Water Security15-Jan-230.99790.73292.0003Water Security
4-Dec-220.99980.69861.9067Water Security22-Jan-230.99750.73572.0079Water Security
11-Dec-220.99970.70681.929Water Security
25.11-week period
8-Aug-210.99671.47675.9007Water Security27-Mar-220.99741.70326.8057Energy Security
15-Aug-210.99721.48495.9334Water Security3-Apr-220.99721.71316.8453Energy Security
22-Aug-210.99761.49285.965Water Security10-Apr-220.99711.72346.8865Energy Security
29-Aug-210.9981.50035.995Water Security17-Apr-220.9971.7346.9288Energy Security
5-Sep-210.99831.50756.0237Water Security24-Apr-220.99681.74486.972Energy Security
12-Sep-210.99851.51436.0509Water Security1-May-220.99671.7567.0167Energy Security
19-Sep-210.99871.52096.0773Water Security8-May-220.99661.76757.0627Energy Security
26-Sep-210.99891.52726.1025Water Security15-May-220.99661.77937.1098Energy Security
3-Oct-210.9991.53346.1272Water Security22-May-220.99651.79147.1582Energy Security
10-Oct-210.99911.53936.1508Water Security29-May-220.99651.80377.2073Energy Security
17-Oct-210.99921.5456.1736Water Security5-Jun-220.99651.81637.2577Energy Security
24-Oct-210.99921.55076.1964Water Security12-Jun-220.99661.82927.3092Energy Security
31-Oct-210.99921.55636.2187Water Security19-Jun-220.99671.84237.3616Energy Security
7-Nov-210.99931.56186.2407Water Security26-Jun-220.99681.85577.4151Energy Security
14-Nov-210.99931.56736.2627Water Security3-Jul-220.99691.86927.469Energy Security
21-Nov-210.99921.57286.2847Energy Security10-Jul-220.99711.8837.5242Energy Security
28-Nov-210.99921.57836.3067Energy Security17-Jul-220.99731.8977.5801Energy Security
5-Dec-210.99921.5846.3294Energy Security24-Jul-220.99751.91117.6365Energy Security
12-Dec-210.99911.58976.3522Energy Security31-Jul-220.99771.92537.6932Energy Security
19-Dec-210.99911.59566.3758Energy Security7-Aug-220.9981.93977.7508Energy Security
26-Dec-210.9991.60166.3998Energy Security14-Aug-220.99821.95427.8087Energy Security
2-Jan-220.99891.60796.4249Energy Security21-Aug-220.99851.96877.8666Energy Security
9-Jan-220.99881.61436.4505Energy Security28-Aug-220.99871.98327.9246Energy Security
16-Jan-220.99871.6216.4773Energy Security4-Sep-220.99881.99777.9825Energy Security
23-Jan-220.99861.62796.5049Energy Security11-Sep-220.9992.01218.0401Energy Security
30-Jan-220.99851.63516.5336Energy Security18-Sep-220.9992.02648.0972Energy Security
6-Feb-220.99841.64256.5632Energy Security25-Sep-220.9992.04058.1535Energy Security
13-Feb-220.99821.65036.5944Energy Security2-Oct-220.99892.05448.2091Energy Security
20-Feb-220.99811.65836.6263Energy Security9-Oct-220.99882.0688.2634Energy Security
27-Feb-220.9981.66666.6595Energy Security16-Oct-220.99852.08138.3166Energy Security
6-Mar-220.99781.67536.6943Energy Security23-Oct-220.99822.09418.3677Energy Security
13-Mar-220.99771.68436.7302Energy Security30-Oct-220.99782.10658.4173Energy Security
20-Mar-220.99751.69366.7674Energy Security6-Nov-220.99732.11848.4648Energy Security
84.45-week period
12-Apr-200.9987−0.0654−0.879Energy Security11-Apr-210.9997−0.0594−0.7984Energy Security
19-Apr-200.9989−0.0616−0.8279Energy Security18-Apr-210.9997−0.0616−0.8279Energy Security
26-Apr-200.999−0.0581−0.7809Energy Security25-Apr-210.9997−0.0638−0.8575Energy Security
3-May-200.9991−0.0548−0.7365Energy Security2-May-210.9996−0.0661−0.8884Energy Security
10-May-200.9992−0.0517−0.6949Energy Security9-May-210.9996−0.0685−0.9207Energy Security
17-May-200.9992−0.0488−0.6559Energy Security16-May-210.9996−0.0708−0.9516Energy Security
24-May-200.9993−0.0461−0.6196Energy Security23-May-210.9996−0.0732−0.9838Energy Security
31-May-200.9994−0.0436−0.586Energy Security30-May-210.9996−0.0757−1.0174Energy Security
7-Jun-200.9995−0.0412−0.5537Energy Security6-Jun-210.9996−0.0782−1.051Energy Security
14-Jun-200.9995−0.0391−0.5255Energy Security13-Jun-210.9995−0.0807−1.0846Energy Security
21-Jun-200.9996−0.0371−0.4986Energy Security20-Jun-210.9995−0.0833−1.1196Energy Security
28-Jun-200.9996−0.0353−0.4744Energy Security27-Jun-210.9995−0.0859−1.1545Energy Security
5-Jul-200.9997−0.0336−0.4516Energy Security4-Jul-210.9995−0.0886−1.1908Energy Security
12-Jul-200.9997−0.0321−0.4314Energy Security11-Jul-210.9995−0.0913−1.2271Energy Security
19-Jul-200.9998−0.0308−0.414Energy Security18-Jul-210.9995−0.094−1.2634Energy Security
26-Jul-200.9998−0.0296−0.3978Energy Security25-Jul-210.9994−0.0968−1.301Energy Security
2-Aug-200.9998−0.0286−0.3844Energy Security1-Aug-210.9994−0.0996−1.3387Energy Security
9-Aug-200.9999−0.0276−0.371Energy Security8-Aug-210.9994−0.1024−1.3763Energy Security
16-Aug-200.9999−0.0269−0.3615Energy Security15-Aug-210.9994−0.1052−1.4139Energy Security
23-Aug-200.9999−0.0262−0.3521Energy Security22-Aug-210.9994−0.1081−1.4529Energy Security
30-Aug-200.9999−0.0257−0.3454Energy Security29-Aug-210.9994−0.111−1.4919Energy Security
6-Sep-200.9999−0.0253−0.34Energy Security5-Sep-210.9993−0.114−1.5322Energy Security
13-Sep-200.9999−0.0251−0.3374Energy Security12-Sep-210.9993−0.117−1.5725Energy Security
20-Sep-201−0.0249−0.3347Energy Security19-Sep-210.9993−0.12−1.6128Energy Security
27-Sep-201−0.0249−0.3347Energy Security26-Sep-210.9993−0.123−1.6532Energy Security
4-Oct-201−0.025−0.336Energy Security3-Oct-210.9993−0.1261−1.6948Energy Security
11-Oct-201−0.0252−0.3387Energy Security10-Oct-210.9993−0.1291−1.7352Energy Security
18-Oct-201−0.0255−0.3427Energy Security17-Oct-210.9993−0.1322−1.7768Energy Security
25-Oct-201−0.0259−0.3481Energy Security24-Oct-210.9992−0.1354−1.8198Energy Security
1-Nov-201−0.0264−0.3548Energy Security31-Oct-210.9992−0.1385−1.8615Energy Security
8-Nov-201−0.027−0.3629Energy Security7-Nov-210.9992−0.1417−1.9045Energy Security
15-Nov-201−0.0276−0.371Energy Security14-Nov-210.9992−0.1449−1.9475Energy Security
22-Nov-201−0.0284−0.3817Energy Security21-Nov-210.9992−0.1481−1.9905Energy Security
29-Nov-200.9999−0.0293−0.3938Energy Security28-Nov-210.9992−0.1514−2.0349Energy Security
6-Dec-200.9999−0.0302−0.4059Energy Security5-Dec-210.9992−0.1546−2.0779Energy Security
13-Dec-200.9999−0.0313−0.4207Energy Security12-Dec-210.9991−0.1579−2.1222Energy Security
20-Dec-200.9999−0.0324−0.4355Energy Security19-Dec-210.9991−0.1612−2.1666Energy Security
27-Dec-200.9999−0.0336−0.4516Energy Security26-Dec-210.9991−0.1645−2.2109Energy Security
3-Jan-210.9999−0.0348−0.4677Energy Security2-Jan-220.9991−0.1679−2.2566Energy Security
10-Jan-210.9999−0.0362−0.4865Energy Security9-Jan-220.9991−0.1712−2.301Energy Security
17-Jan-210.9999−0.0376−0.5054Energy Security16-Jan-220.9991−0.1746−2.3467Energy Security
24-Jan-210.9999−0.039−0.5242Energy Security23-Jan-220.9991−0.178−2.3924Energy Security
31-Jan-210.9998−0.0406−0.5457Energy Security30-Jan-220.9991−0.1814−2.4381Energy Security
7-Feb-210.9998−0.0422−0.5672Energy Security6-Feb-220.999−0.1848−2.4838Energy Security
14-Feb-210.9998−0.0439−0.59Energy Security13-Feb-220.999−0.1882−2.5295Energy Security
21-Feb-210.9998−0.0456−0.6129Energy Security20-Feb-220.999−0.1917−2.5765Energy Security
28-Feb-210.9998−0.0474−0.6371Energy Security27-Feb-220.999−0.1951−2.6222Energy Security
7-Mar-210.9998−0.0493−0.6626Energy Security6-Mar-220.999−0.1986−2.6693Energy Security
14-Mar-210.9998−0.0512−0.6881Energy Security13-Mar-220.999−0.2021−2.7163Energy Security
21-Mar-210.9997−0.0532−0.715Energy Security20-Mar-220.999−0.2056−2.7633Energy Security
28-Mar-210.9997−0.0552−0.7419Energy Security27-Mar-220.999−0.2091−2.8104Energy Security
4-Apr-210.9997−0.0573−0.7701Energy Security3-Apr-220.999−0.2126−2.8574Energy Security


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Figure 1. Interpretation of phase difference between signals x and y [32].
Figure 1. Interpretation of phase difference between signals x and y [32].
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Figure 2. The Google Search hits for the keywords “Food Security”, “Energy Security”, and “Water Security” over the last 5 years.
Figure 2. The Google Search hits for the keywords “Food Security”, “Energy Security”, and “Water Security” over the last 5 years.
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Figure 3. Wavelet power spectrum for “Food Security”, “Water Security”, and “Energy Security” Google Trends. White contours show significant powers at α = 0.1 significance level.
Figure 3. Wavelet power spectrum for “Food Security”, “Water Security”, and “Energy Security” Google Trends. White contours show significant powers at α = 0.1 significance level.
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Figure 4. Wavelet coherence and phase difference angles (arrows) for “Food Security”, “Water Security”, and “Energy Security” interests. White contours show significant powers at α = 0.1 significance level (90% confidence level). Phase difference angles are only presented for locations with significant coherence.
Figure 4. Wavelet coherence and phase difference angles (arrows) for “Food Security”, “Water Security”, and “Energy Security” interests. White contours show significant powers at α = 0.1 significance level (90% confidence level). Phase difference angles are only presented for locations with significant coherence.
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Table 1. Comparison of trend extraction features in popular search engines.
Table 1. Comparison of trend extraction features in popular search engines.
Data AccessibilityExtensive data availabilityGood data accessibilityLimited data accessibility
Trend AnalysisStrong trend analysis toolsDecent trend analysisLimited trend analysis
Real-time DataProvides real-time dataOffers real-time dataLimited real-time data
Geographic SpecificityOffers precise location dataProvides location-based resultsLimited location data
Data VisualizationOffers data visualization toolsBasic data visualizationLimited data visualization
API SupportRobust API for data accessAPI support availableLimited API support
CustomizationCustomizable search parametersSome customization optionsLimited customization
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Hassani, H.; Komendantova, N.; Rovenskaya, E.; Yeganegi, M.R. Social Trend Mining: Lead or Lag. Big Data Cogn. Comput. 2023, 7, 171.

AMA Style

Hassani H, Komendantova N, Rovenskaya E, Yeganegi MR. Social Trend Mining: Lead or Lag. Big Data and Cognitive Computing. 2023; 7(4):171.

Chicago/Turabian Style

Hassani, Hossein, Nadejda Komendantova, Elena Rovenskaya, and Mohammad Reza Yeganegi. 2023. "Social Trend Mining: Lead or Lag" Big Data and Cognitive Computing 7, no. 4: 171.

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