Query Popularity Research Articles

Digital promotion of Kazakhstan business universities for generation Z applicants

Object: to determine the assessment of the effectiveness of digital channels for promoting educational services of business universities for representatives of Generation Z. Methods: When writing the article, the methods of analysis, synthesis, description, expert assessment, generaliza tion using analytical web tools similarweb.com, pagespeed.web.dev, wordstat.yandex.ru were used. The analysis of the websites of leading business universities of Kazakhstan was carried out by performance parameters, search engine op timization, recommendations, special features, number of visits, targeting by regions and countries, audience de mographics, marketing channels. An assessment of the popularity of queries by the name of universities in the Eurasian space was made. An assessment of the search queries of visitors to the websites of business universities was carried out. Findings: based on the conducted research, key and additional channels for promoting educational services of Ka zakhstani business universities, in demand by zoomers, were proposed. In a competitive environment, the concentration of efforts to promote educational services of business universities is focused on significant and effective marketing channels. A review of marketing channels leading to university websites allows us to draw conclusions about the un derutilization of social networks. Conclusions: focusing on the proposed digital channels will allow business universities to focus their efforts on popular marketing channels, ensure an increase in the conversion rate, and transform the potential target market from regional to international.

Перспективы развития научно-популярного туризма в Арктике

The development of the Arctic territories of the Russian Federation is especially relevant in an unstable geopolitical situation, due to the importance of the region for national security. One of the prospective directions of Arctic exploration is the development of popular science tourism. The aim of the work is to determine the vector of development of popular science tourism in the subjects of the Arctic zone of the Russian Federation in order to increase the scientific and technological potential of Russia, strengthen the presence of the Russian Federation in the Arctic, and attract young people to research projects related to the sustainable development of the Russian Arctic. As a result of the analysis of the theoretical aspects of popular science tourism, its essence and principles were clarified, and the relevance of the study was revealed based on the popularity of Internet queries. With the help of the "sales funnel", a model of involving potential stakeholders in science is built. A study of the current situation in the field of popular science tourism has revealed its insufficient distribution in the Arctic zone of the Russian Federation. To determine the most attractive Arctic regions in order to develop popular science tourism, an analysis of the dynamics and structure of indicators characterizing the tourist potential of the territory, which formed the basis for the point rating assessment of the regions included in the Arctic zone of the Russian Federation, was carried out. According to the results of the study, it was revealed that the Krasnoyarsk Territory, the Republic of Sakha (Yakutia), the Republic of Karelia and the Murmansk Region.

DYNAMICS OF PUBLIC INTEREST IN PUBLIC DEBT MANAGEMENT IN THE CONTEXT OF ENSURING ECONOMIC SECURITY

The article analyzes the trends in the dynamics of public interest in public debt management in the context of ensuring debt and economic security. The methodology is based on using the Google Trends web tool and the corresponding scale for measuring the popularity of search queries – Google Trends Scale. Using Google Trends line charts, a trend analysis was carried out using the keywords «economic security», «public debt management» and «debt sustainability». The key parameters of the analysis are: 1) timeframe: from August 2019 to August 2024; 2) geographical boundaries: Ukraine and the whole world, with a special emphasis on the EU countries. The article proposes to assess the interest of Internet users within three levels on the GT scale (low (0-29 points), medium (30-74 points) and high (75-100)).

ON THE PROBLEM OF FORMATION OF ECOLOGICAL CONSCIOUSNESS IN POST-NORMAL TIMES: THE EXPERIENCE OF DISCOURSE ANALYSIS

Human loves comfort. Throughout the history of his existence, he transforms the environment for his comfort. As a result, we live in the Anthropocene epoch, in which Homo sapience impacts the planet's geological processes. Maintaining an ecological balance is vital for the human race’s survival. One of the ways to organize a well-balanced urban environment is to analyze the way how the public communicates this problem, paying attention to the specific circumstances of a particular place. The methodology of the electronic sociological survey was used, as well as work with Google Trends to analyze the popularity of search queries on environmental issues in Almaty. This article, on the basis of an interdisciplinary study, analyzed the formation and development of the mass media discourse on urban ecologization in Almaty. The paper concludes that the ecological discourse form not only the main vector of the media content but also ecological consciousness since the greening of cities is impossible without the development of the ecology culture and ecology thinking.

Minor extensions of the logistic equation for growth curves of word counts on online media: parametric observation of diversity of growth in society

To understand the growth phenomena in collective human systems, we analyzed monthly word count time series of new vocabularies extracted from approximately 1 billion Japanese blog articles from 2007 to 2019. In particular, we first introduced the extended logistic equation by adding one parameter to the original equation and showed that the model can consistently reproduce various patterns of actual growth curves, such as the logistic function, linear growth, and finite-time divergence. Second, by analyzing the model parameters, we found that the typical growth pattern is not only a logistic function, which often appears in various complex systems, but also a non-trivial growth curve that starts with an exponential function and asymptotically approaches a power function without a steady state. We also observed a connection between the functional form of growth and the peak-out behavior. Finally, we showed that the proposed model and statistical properties are also valid for Google Trends data (English, French, Spanish, and Japanese), which is a time series of the nationwide popularity of search queries.

Impact assessment of immunization and the COVID-19 pandemic on varicella across Europe using digital epidemiology methods: A descriptive study.

Varicella is usually a mild disease in children but may be life-threatening, especially in adolescents and adults. Infection control measures implemented during the Coronavirus Disease 2019 (COVID-19) pandemic may have suppressed varicella transmission, potentially creating an 'immunity debt', particularly in countries without universal varicella vaccination. To assess trends in Google search engine queries for varicella keywords as a proxy for varicella infection rates and to evaluate the effect of universal varicella vaccination on these trends. A further objective was to assess the impact of the COVID-19 pandemic on varicella keyword search query trends in countries with and without universal varicella vaccination. This study used the keyword research tool, Google Trends, to evaluate trends in time series of the relative search query popularity of language-specific varicella keywords in 28 European countries from January 2015 through December 2021. The Google Ads Keyword Planner tool was used to evaluate absolute search volumes from March 2018 through December 2021. The relative search query popularity of varicella keywords displayed marked seasonal variation. In all 28 countries, the relative search query popularity of varicella keywords declined after the start of the COVID-19 pandemic (March 2020), compared with pre-pandemic levels (range, -18% to -70%). From April 2020 to July 2021, a period of intense COVID-19 transmission and infection control, absolute search volumes for varicella keywords were lower than pre-pandemic levels but rebounded after July 2021, when infection control measures were relaxed. This evaluation of search query trends demonstrated that search query data could be used as a proxy for trends in varicella infection rates and revealed that transmission of varicella may have been suppressed during the COVID-19 pandemic. Consideration should be given to using search query data to better understand the burden of varicella, particularly in countries where surveillance systems are inadequate.

Методика региональной оценки цифрового неравенства третьего уровня

The article describes and analyzes the three levels of digital inequality in society currently identified, and examines the world experience of their assessment. It is shown that the third level of digital inequality is the most difficult for research. It represents an uneven distribution of opportunities for obtaining important results in real life (employment, education, participation in political life, etc.) through the use of modern technologies. The authors have developed a methodology for the regional quantitative assessment of third-level digital inequality by analyzing the popularity of Google search query topics related to three categories: “Everyday services and services”, “Education, science and technology”, “Entertainment”. These categories are highlighted as markers for evaluating the effectiveness of Internet use by the population. At the same time, an increased search interest in entertainment indicates a lower efficiency of Internet use, and a higher interest in services and education. The indexes of popularity of search queries for each of the categories are calculated and then, based on them, an integral index of the efficiency of Internet use in the regions of Russia is calculated. Statistical data for the study were collected using the Google Trends service for 2016–2020. All calculated indicators are presented on maps, their territorial features are identified and described. The lowest level of Internet use efficiency is observed in the regions of the North Caucasus, and the highest is in the Republics of Tatarstan and Sakha (Yakutia), as well as in Moscow and St. Petersburg.

Changes in Air Pollution-Related Behaviour Measured by Google Trends Search Volume Index in Response to Reported Air Quality in Poland.

Decreased air quality is connected to an increase in daily mortality rates. Thus, people’s behavioural response to sometimes elevated air pollution levels is vital. We aimed to analyse spatial and seasonal changes in air pollution-related information-seeking behaviour in response to nationwide reported air quality in Poland. Google Trends Search Volume Index data was used to investigate Poles’ interest in air pollution-related keywords. PM10 and PM2.5 concentrations measured across Poland between 2016 and 2019 as well as locations of monitoring stations were collected from the Chief Inspectorate of Environmental Protection databases. Pearson Product-Moment Correlation Coefficients were used to measure the strength of spatial and seasonal relationships between reported air pollution levels and the popularity of search queries. The highest PM10 and PM2.5 concentrations were observed in southern voivodeships and during the winter season. Similar trends were observed for Poles’ interest in air pollution-related keywords. Greater interest in air quality data in Poland strongly correlates with both higher regional and higher seasonal air pollution levels. It appears that Poles are socially aware of this issue and that their intensification of the information-seeking behaviour seems to indicate a relevant ad hoc response to variable threat severity levels.

Awareness of Sustainable Use of National Natural Resources as a Basis for the Environmental Protection Systems: Economic Aspects and the New Global Index of the Popularity of Environmental Queries (GIPEQ)

Awareness of Sustainable Use of National Natural Resources as a Basis for the Environmental Protection Systems: Economic Aspects and the New Global Index of the Popularity of Environmental Queries (GIPEQ)

Keyword Selection Strategies in Search Engine Optimization: How Relevant is Relevance?

We build an empirical framework using search queries and organic click data which provides model-based guidance to SEO practitioners for keyword selection and web content creation. Specifically, we study how search characteristics (search query popularity, search query competition, search query specificity, and search intent) and website characteristics (content relevance and online authority) interact to affect the expected organic clicks as well as the organic rank a website receives from the search engine result page (SERP). It is often thought that content relevance is a key factor to improve the effectiveness of SEO. We find, however, that content relevance is an important factor in driving organic clicks only when the consumer is farther along in the customer journey and searching for ways to purchase a product. Whereas, when the customer is at the awareness stage and looking for product information, online authority is the key driver of organic clicks.

FINANCIAL RISK MANAGEMENT OF AN INDUSTRIAL ENTERPRISE

The article is devoted to the issue of financial risk management of industrial enterprises, which is relevant in the modern realities of the Ukrainian economy. The Google Trends web application analyzes the popularity of search queries in business and industry for phrases such as "financial risk" and "financial risk management" in Ukraine and in the world as a whole over the past 12 months. Based on official statistics, the analysis of the dynamics of the main financial indicators of industrial enterprises of Sumy region for 5 years, namely financial results before tax, financial results from operating activities and operating profitability. Based on the coefficient of variation, the level of financial risk that accompanies the activities of industrial enterprises of Sumy region is determined. The essence of financial risks of an industrial enterprise is revealed and its constituent elements are presented. The main existing approaches to the quantitative assessment of financial risks are presented, which are mainly based on probabilistic assessment. A methodical approach to financial risk management of industrial enterprises is proposed, which provides for constant monitoring of deviations of the integrated indicator, calculated on the basis of financial statements of the enterprise and includes indicators of liquidity, financial stability, profitability, cash flow from financial activities. At the same time, the company is recommended to set clear boundaries of possible deviations of the integrated financial indicator. Emphasis is also placed on the need to form and implement an integrated financial risk management system at industrial enterprises of Ukraine in the modern economic realities of Ukraine. A clear system of financial risk management will allow industrial enterprises to respond in a timely manner to potential threats, neutralize financial risks and avoid possible losses.

Global trends in the awareness of sepsis: insights from search engine data between 2012 and 2017

BackgroundSepsis is an established global health priority with high mortality that can be curtailed through early recognition and intervention; as such, efforts to raise awareness are potentially impactful and increasingly common. We sought to characterize trends in the awareness of sepsis by examining temporal, geographic, and other changes in search engine utilization for sepsis information-seeking online.MethodsUsing time series analyses and mixed descriptive methods, we retrospectively analyzed publicly available global usage data reported by Google Trends (Google, Palo Alto, CA, USA) concerning web searches for the topic of sepsis between 24 June 2012 and 24 June 2017. Google Trends reports aggregated and de-identified usage data for its search products, including interest over time, interest by region, and details concerning the popularity of related queries where applicable. Outlying epochs of search activity were identified using autoregressive integrated moving average modeling with transfer functions. We then identified awareness campaigns and news media coverage that correlated with epochs of significantly heightened search activity.ResultsA second-order autoregressive model with transfer functions was specified following preliminary outlier analysis. Nineteen significant outlying epochs above the modeled baseline were identified in the final analysis that correlated with 14 awareness and news media events. Our model demonstrated that the baseline level of search activity increased in a nonlinear fashion. A recurrent cyclic increase in search volume beginning in 2012 was observed that correlates with World Sepsis Day. Numerous other awareness and media events were correlated with outlying epochs. The average worldwide search volume for sepsis was less than that of influenza, myocardial infarction, and stroke.ConclusionsAnalyzing aggregate search engine utilization data has promise as a mechanism to measure the impact of awareness efforts. Heightened information-seeking about sepsis occurs in close proximity to awareness events and relevant news media coverage. Future work should focus on validating this approach in other contexts and comparing its results to traditional methods of awareness campaign evaluation.

Beyond traditional collaborative search: Understanding the effect of awareness on multi-level collaborative information retrieval

Although there has been a great deal of research into Collaborative Information Retrieval (CIR) and Collaborative Information Seeking (CIS), the majority has assumed that team members have the same level of unrestricted access to underlying information. However, observations from different domains (e.g. healthcare, business, etc.) have suggested that collaboration sometimes involves people with differing levels of access to underlying information. This type of scenario has been referred to as Multi-Level Collaborative Information Retrieval (MLCIR). To the best of our knowledge, no studies have been conducted to investigate the effect of awareness, an existing CIR/CIS concept, on MLCIR. To address this gap in current knowledge, we conducted two separate user studies using a total of 5 different collaborative search interfaces and 3 information access scenarios. A number of Information Retrieval (IR), CIS and CIR evaluation metrics, as well as questionnaires were used to compare the interfaces. Design interviews were also conducted after evaluations to obtain qualitative feedback from participants. Results suggested that query properties such as time spent on query, query popularity and query effectiveness could allow users to obtain information about team's search performance and implicitly suggest better queries without disclosing sensitive data. Besides, having access to a history of intersecting viewed, relevant and bookmarked documents could provide similar positive effect as query properties. Also, it was found that being able to easily identify different team members and their actions is important for users in MLCIR. Based on our findings, we provide important design recommendations to help develop new CIR and MLCIR interfaces.

Term-level semantic similarity helps time-aware term popularity based query completion

Query completion service, normally known in the form of query auto completion (QAC) and widely provided by common search engines, assists users to formulate their queries after only typing few keystrokes. Previous work on QAC basically ranks query candidates according to their query popularity whic h is collected from the search logs, ignoring the internal semantic similarity between terms inside a query. However, we argue semantically related terms are apt to be combined when generating a query. In addition, as users often engage in QAC at word boundary (i.e., after typing a full word), we suppose that the time-aware popularity of the first word in a query candidate could affect the ranking of QAC candidates. Hence, based on the Markov assumption, we propose a new QAC ranking method, which models the QAC engagement as a Markov Chain and takes the semantic similarity between query terms into account. We contrast our proposed model with the traditional query popularity-based QAC approaches and verify its effectiveness in terms of Mean Reciprocal Rank (MRR). The experimental results show that our model significantly outperforms the baselines, achieving an average MRR improvement around 4% over the baselines.

Stock price forecasting taking into account the popularity of search queries

The article proposes to study the impact of behavioral factors on forecasting the stock price, and presents the model of the above-mentioned forecast. The factors that are supposed to be taken into account are used as indicators of the popularity of search queries within the certain topic. Two companies (Apple and Royal Dutch Shell) were selected for research, because they have different development histories. The Glossary based on papers on this subject has been created; it consists of 67 terms of economic, social and political meaning reflecting sentiment-oriented behaviors of traders, and the criteria for search of high popularity queries have been determined. We assume that the queries characterized by high popularity index make a great impact on the dynamics of the stock price. Besides, the article addresses the issue of neural networks as upon researching we observe that they might serve as indicators of the popularity of search queries. Furthermore, we introduced the forecast for two companies from different sectors of the economy. In addition, forecasting was made with various combinations of these search queries, which were combined according to the semantic load. During the experiments it was revealed that for the information-sector company the quality of the model increased significantly due to the inclusion of behavioral factors, while for the processing-sector company the data did not significantly improve the forecast. This follows from the specificity of the study. The results can indicate the adequacy of the constructed models and confirm the feasibility of using the popularity indicators of search queries for forecasting the stock price.

Exploiting Query’s Temporal Patterns for Query Autocompletion

Query autocompletion (QAC) is a common interactive feature of web search engines. It aims at assisting users to formulate queries and avoiding spelling mistakes by presenting them with a list of query completions as soon as they start typing in the search box. Existing QAC models mostly rank the query completions by their past popularity collected in the query logs. For some queries, their popularity exhibits relatively stable or periodic behavior while others may experience a sudden rise in their query popularity. Current time-sensitive QAC models focus on either periodicity or recency and are unable to respond swiftly to such sudden rise, resulting in a less optimal QAC performance. In this paper, we propose a hybrid QAC model that considers two temporal patterns of query’s popularity, that is, periodicity and burst trend. In detail, we first employ the Discrete Fourier Transform (DFT) to identify the periodicity of a query’s popularity, by which we forecast its future popularity. Then the burst trend of query’s popularity is detected and incorporated into the hybrid model with its cyclic behavior. Extensive experiments on a large, real-world query log dataset infer that modeling the temporal patterns of query popularity in the form of its periodicity and its burst trend can significantly improve the effectiveness of ranking query completions.

Prefix-Adaptive and Time-Sensitive Personalized Query Auto Completion

Query auto completion (QAC) methods recommend queries to search engine users when they start entering a query. Current QAC methods mostly rank query completions based on their past popularity, i.e., on the number of times they have previously been submitted as a query. However, query popularity changes over time and may vary drastically across users. Accordingly, the ranking of query completions should be adjusted. Previous time-sensitive and user-specific QAC methods have been developed separately, yielding significant improvements over methods that are neither time-sensitive nor personalized. We propose a hybrid QAC method that is both time-sensitive and personalized. We extend it to handle long-tail prefixes, which we achieve by assigning optimal weights to the contribution from time-sensitivity and personalization. Using real-world search log datasets, we return top <inline-formula><tex-math notation="LaTeX"> $N$</tex-math> </inline-formula> query suggestions ranked by predicted popularity as estimated from popularity trends and cyclic popularity behavior; we rerank them by integrating similarities to a user's previous queries (both in the current session and in previous sessions). Our method outperforms state-of-the-art time-sensitive QAC baselines, achieving total improvements of between 3 and 7 percent in terms of mean reciprocal rank (MRR). After optimizing the weights, our extended model achieves MRR improvements of between 4 and 8 percent.

Temporal Dynamics in Information Retrieval

Since time is an omnipresent feature of our existence, many elements of time are embedded in information itself, and related behaviours such as creation, seeking and utilisation. In IR, time can distinguish the interpretation of information, and influence the intentions and expectations of users' information seeking activity. Many time-based patterns and trends - namely temporal dynamics - are evident in streams of information behaviour by individuals and crowds. A temporal dynamic refers to a periodic regularity, or, a one-off or irregular past, present or future of a particular element (e.g., word, topic or query popularity) - driven by predictable and unpredictable time-based events and phenomena. Several challenges and opportunities related to temporal dynamics emerge in IR. This thesis explores temporal dynamics from the perspective of (i) query popularity and meaning, and (ii) word use and relationships over time. In particular, I consider how real-time temporal dynamics in information seeking should be supported for consistent user satisfaction over time, and moreover, how previously observed temporal dynamics offer a complementary dimension which can be exploited to inform more effective IR systems. Uncertainty about user expectations is a perennial problem for IR systems, further confounded by changes over time. Addressing this, IR systems can either assist the user to submit an effective query (e.g., error-free and descriptive), or better anticipate what the user is most likely to want in relevance ranking. I first explore methods to always help users formulate queries with time-aware query auto-completion capable of suggesting both recent and always popular queries. I propose and evaluate several novel approaches, and demonstrate state-of-the-art performance of up to +9.2% improvement above existing baselines for diverse search scenarios in different languages. Furthermore, I explore the impact of temporal dynamics on the motives behind users' information seeking, and thus how relevance itself is subject to temporal dynamics. I find the most likely meaning of ambiguous queries is affected over short and long-term periods (e.g., hours to months) by several periodic and oneoff event-driven temporal dynamics. Finally, I find that for many event-driven multi-faceted queries, relevance can often be inferred by modelling the temporal dynamics of changes in related information. IR approaches are typically based on methods which characterize the nature of information through the statistical distributions of words and phrases. I model and exploit the temporal dimension of the collection, captured by temporal dynamics, in these established IR approaches. I explore how the temporal dynamic similarity of word and phrase use in a collection can be exploited to infer temporal semantic relationships between the terms. I propose an approach to uncover a query topic's "chronotype" terms -- that is, its most distinctive and temporally interdependent terms, based on a mix of temporal and non-temporal evidence. Experiments demonstrate that exploiting chronotype terms in temporal query expansion leads to significantly improved retrieval performance in several time-based collections. Temporal dynamics provide both a challenge and an opportunity for IR systems. Overall, this thesis demonstrates that temporal dynamics can be used to derive tacit structure and meaning of information and information behaviour, which is valuable for improving timeaware IR system effectiveness.

Learning from homologous queries and semantically related terms for query auto completion

Query auto completion (QAC) models recommend possible queries to web search users when they start typing a query prefix. Most of today’s QAC models rank candidate queries by popularity (i.e., frequency), and in doing so they tend to follow a strict query matching policy when counting the queries. That is, they ignore the contributions from so-called homologous queries, queries with the same terms but ordered differently or queries that expand the original query. Importantly, homologous queries often express a remarkably similar search intent. Moreover, today’s QAC approaches often ignore semantically related terms. We argue that users are prone to combine semantically related terms when generating queries.We propose a learning to rank-based QAC approach, where, for the first time, features derived from homologous queries and semantically related terms are introduced. In particular, we consider: (i) the observed and predicted popularity of homologous queries for a query candidate; and (ii) the semantic relatedness of pairs of terms inside a query and pairs of queries inside a session. We quantify the improvement of the proposed new features using two large-scale real-world query logs and show that the mean reciprocal rank and the success rate can be improved by up to 9% over state-of-the-art QAC models.

The Dark Side of Internet Searches: A Macro Level Assessment of Rape Culture

Introduction.28 seconds, the total time the Google search engine takes to deliver nearly onehundred thousand websites for the search query rape The intent of this statement is not to reflect in availability of material or suggest the search reflects those looking for, in fact, the best rape scenes. Rather being able to identify these search queries and track the trends and popularity of user queries is methodologically worthy of inquiry. Researchers agree the Internet is an influential source of acquisition and replication of norms, behavior, and attitudes (Jaishankar, 2008; Yar, 2006). The evolving nature of these networks have fundamentally influenced production of knowledge, distribution of tasks, and activities society engages, shaping the perspectives of generations of individuals (Castells, 1996). While research continues to study the relative strength of the Internet within acquisition and reinforcement, it has remained a consistent source of interest within human development (Greenfield & Yan, 2006). Having access to track interesting search queries overtime or following specific events presents as an important source of data, one that may undoubtedly provide new insight into human behavior.Currently there are ~275 million Internet users in North America and ~2.4 billion Internet users globally (Miniwatts Marketing Group, 2013), making the study of this tool incredibly relevant to social scientists. Introducing new methods has the capacity to benefit and reinvigorate discussion on sexual violence and foster the emergence of new perspectives. The increasing use of the Internet and resulting immersion into the daily lives of users would prove an important data source, specifically if the data allowed a longitudinal analysis, which controlled for population changes. For example, if Internet queries for how to be a better parent gradually increased 50-percent over a ten-year period, or racist jokes decreased 35-percent, a nation and its people would offer signs of change.In the past, access to violent, heterosexual pornography was largely limited to pornographic magazines or dark curtained rooms in video stores. Growth in the Internet and diverse nature of Internet users has fostered spread of not merely pornography, but the growth of niche pornography. As pornography becomes more socially acceptable, accessible, and marketed to wider audiences, its role in perpetuating misogynistic behaviors and attitudes thereby increases (Jensen, 2011; Picker & Sun, 2008). Scholars have suggested that pornography has a prominent feature within acquisition of behaviors and attitudes that correlate increasingly with acceptance, reproduction, and transference of what scholars have voiced as 'rape culture' (Miller & Biele, 1993).Conceptualized and widely studied across disciplines since the late 1980s, 'rape culture' research concerns itself with operationalizing, evaluating, and mediating acquisition of the associated attitudes, behaviors, and norms correlated with violence against women (Herman, 1989). Researchers suggested that violence against women is multifaceted; reflecting the interplay between personal, situational, and socio-cultural factors (Heise, 1998, p. 263). The research took multiple paths, but as Heise (1998) offers, it was important that scholars understand the primacy of culturally constructed messages promoting and promulgating violence against women in a society. Therefore, it was important to understand further the acquisition, modes of transmission, and reinforcement of these intricate markers (attitudes, behaviors, and norms) for rape culture.The resulting research produced important insight into rape culture and sexual violence. Researchers have explained the characteristics reflected at a micro and macro level for prolongation of misogyny and manifestation of rape culture in society. However, accurately measuring individual characteristics within the current studies is limited to survey instruments and focus groups, or at a macro level, official crime statistics, surveys of victims, or content analysis of popular media. …