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campaign. By creating thousands of data points on 230 million U.S. adults, the data mining company had the potential to analyze which individuals could be swayed into voting for the Trump campaign, and then send messages or advertisements to said targets and influence user mindset. Specific target voters could then be exposed to pro-Trump messages without being aware, even, of the political influence settling on them. Such a specific form of targeting in which select individuals are introduced to an above-average amount of campaign advertisement is referred to as "micro-targeting." There remains great controversy in measuring the amount of influence this micro-targeting had in the 2016 elections. The impact of micro-targeting ads and social media data analytics on politics is unclear as of the late 2010s, as a newly arising field of technology.
465:, if we take a large amount of data around computer professionals, say the "IT architect", and built a word cloud, no doubt the largest word in the cloud would be "architect". This analysis is also about tool usage. Some tools may do a good job at determining sentiment, where as others may do a better job at breaking down text into a grammatical form that enables us to better understand the meaning and use of various words or phrases. In performing analytic analysis, it is difficult to enumerate each and every step to take on an analytical journey. It is very much an iterative approach as there is no prescribed way of doing things.
726:-based approach to collecting, analyzing, and interpreting social media data. Social media presents a promising, albeit challenging, source of data for business intelligence. Customers voluntarily discuss products and companies, giving a real-time pulse of brand sentiment and adoption. Social media is one of the most important tools for marketers in the rapidly evolving media landscape. Firms have created specialized positions to handle their social media marketing. These arguments are in line with the literature on social media marketing that suggests that social media activities are interrelated and influence each other.
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trying to understand the public's perception of a certain topic as it unfolding to allow for reaction or an immediate change in course. In near real-time analysis, we assume that data is ingested into the tool at a rate that is less than real-time. Ad hoc analysis is a process designed to answer a single specific question. The product of ad hoc analysis is typically a report or data summary. A deep analysis implies an analysis that spans a long time and involves a large amount of data, which typically translates into a high CPU requirement.
442:, etc. The data analysis step begins once we know what problem we want to solve and know that we have sufficient data that is enough to generate a meaningful result. How can we know if we have enough evidence to warrant a conclusion? The answer to this question is: we don't know. We can't know this unless we start analyzing the data. While analyzing if we found the data isn't sufficient, reiterate the first phase and modify the question. If the data is believed to be sufficient for analysis, we need to build a data model.
558:. A highly complex tool produces more amounts of details. The second type of analysis in the context of velocity is an analysis of data at rest. This analysis is performed once the data is fully collected. Performing this analysis can provide insights such as: which of your company's products has the most mentions as compared to others? What is the relative sentiment around your products as compared to a competitor's product?
734:. In a similar vein, suspicious social media postings have significantly increased along with the growth of social media. Luca and Servas (2015) reported that firms have a potential incentive to use fake postings when they have increased competition. Therefore, upgrading our ability to identify and monitor suspicious postings (e.g., fake reviews on Yelp) has become an important part of social media platform management.
591:, set up a goal, which is to convey great quantities of information in a format that is easily assimilated by the consumer of information. It is important to answer "Who is the audience?", and "Can you assume the audience has the knowledge of terminologies used?" An audience of experts will have different expectations than a general audience; therefore, the expectations have to be considered.
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480:, ad hoc analysis on accumulated data or deep analysis performed on accumulated data. This analysis dimension is really driven by the amount of time available to come up with the results of a project. This can be considered as a broad continuum, where the analysis time ranges from few hours at one end to several months at the other end. This analysis can answer following type of questions:
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311:, and information interpretation. To maximize the value derived at every point during the process, analysts may define a question to be answered. The important questions for data analysis are: "Who? What? Where? When? Why? and How?" These questions help in determining the proper data sources to evaluate, which can affect the type of analysis that can be performed.
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299:. It is commonly used by marketers to track online conversations about products and companies. One author defined it as "the art and science of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making."
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in a reasonable time period. Capacity numbers need to address not only the CPU needs but also the network capacity needed to retrieve data. This analysis could be performed as real-time, near real-time, ad hoc exploration and deep analysis. Real-time analysis in social media is an important tool when
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from an unprocessed data, we need to start processing it, refine the dataset by including data that we want to focus on, and organize data to identify information. In the context of social media analytics, data identification means "what" content is of interest. In addition to the text of content, we
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Cambridge
Analytica attracted controversy for its use of data gathered from social media. A similar case took place in which a breach and Facebook data was acquired by Cambridge Analytica. There was concern that they had used the data to encourage British citizens to vote to leave the European Union
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The insights derived from analysis can be as varied as the original question that was posed in step one of analysis. At this stage, as the nontechnical business users are the receivers of the information, the form of presenting the data becomes important. How could the data make sense efficiently so
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While this was a breach of user privacy, data mining and targeted marketing undermined the public accountability to which social media entities are no longer subject, therefore twisting the democratic election system and allowing it to be dominated by platforms of “user-generated content polarized
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for individuals and the legal boundaries to be created for data science companies in relevance to politics in the future. Both of the examples listed below demonstrate a future in which big data can change the game of international politics. It is likely politics and technology will evolve together
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of available data to focus on for analysis. Raw data is useful once it is interpreted. After data has been analyzed, it can begin to convey a message. Any data that conveys a meaningful message becomes information. On a high level, unprocessed data takes the following forms to translate into exact
737:
Muruganantham and Gandhi (2020) proposed a Multi-Criteria
Decision Making (MCDM) model to prove that social media users' preferences, sentiments, behavior, and marketing data are related to social media analytics. Internet users are closely connected and show a high degree of mutual influence in
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and the
Cambridge University Psychometric Centre were accused of, then investigated by the British government for their possible abuse of data to promote unlawful campaign techniques for Brexit. The referendum ended with 51.89% of voters supporting the withdrawal of the United Kingdom from the
729:
Moon and
Iacobucci (2022) focused on the marketing applications of social media analytics. Such applications include consumer behavior on social media, social media impact on firm performance, business strategy, product/brand management, social media network analysis, consumer privacy and data
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is changing about the players during the course of match? Is the crowd conveying positive sentiment about the player who is actually losing the game? In these cases, the analysis is done as arrives. In this analysis, the amount of detail produced is directly correlated to the complexity of the
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that followed the
American presidential election of 2016 was one involving a three-way relationship between Cambridge Analytica, the Trump campaign, and Facebook. Cambridge Analytica acquired the data of over 87 million unaware Facebook users and analyzed the data for the benefit of the Trump
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Domain of
Analysis: The domain of the analysis is broadly classified into external social media and internal social media. Most of the time when people use the term social media, they mean external social media. This includes content generated from popular social media sites such as
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Set up a clear framework: the analyst needs to ensure that the visualization is syntactically and semantically correct. For example, when using an icon, the element should bear resemblance to the thing it represents, with size, color, and position all communicating meaning to the
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In the analysis of our data, it's handy to have several tools available at our disposal to gain a different perspective on discussions taking place around the topic. The aim here is to configure the tools to perform at peak for a particular task. For example, thinking about a
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message: noisy data; relevant and irrelevant data, filtered data; only relevant data, information; data that conveys a vague message, knowledge; data that conveys a precise message, wisdom; data that conveys exact message and reason behind it. To derive
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434:. In other words, data analysis is the phase that takes filtered data as input and transforms that into information of value to the analysts. Many different types of analysis can be performed with social media data, including analysis of posts,
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security on social media, and fictitious/biased content on social media. In particular, consumer privacy and data security are becoming more and more important in the social media universe given the increasing risk stemming from social media
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1980:
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367:: It is important to ensure that the data included in the analysis is only from that region of the world where the analysis is focused on. For example, if the goal is to identify the clean water problems in
1610:
Craig
Silverman, Craig Timberg, Jeff Kao and Jeremy B. Merrill. (4 January 2022). "Facebook Hosted Surge of Misinformation and Insurrection Threats in Months Leading Up to January. 6 Attack, Records Show".
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throughout the next century. In the cases with
Cambridge Analytica, the effects of social media analytics have resonated throughout the globe through two major world powers, the United States and the U.K.
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want to know: who wrote the text? Where was it found or on which social media venue did it appear? Are we interested in information from a specific locale? When did someone say something in social media?
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should package information into a structure that is presented as a narrative and easily remembered. This is important in many scenarios when the analyst is not the same person as a decision-maker.
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and relationships contain the data. Exposure of the patterns and understating them play a key role in decision making process. Mainly there are three criteria to consider in visualizing data.
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953:. After a three-year investigation it was concluded in 2020 that there had been no involvement in the referendum. Besides Cambridge Analytica, several other data companies such as
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is a process or method that we use to organize data elements and standardize how the individual data elements relate to each other. This step is important because we want to run a
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Analysis of
Facebook political groups and postings by social media analytics firm, CounterAction, have shown the role of social media giants in protest movements such as
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Craig
Timberg, Elizabeth Dwoskin, and Reed Albergotti. (22 October 2021). "Inside Facebook, January. 6 violence fueled anger, regret over missed warning signs".
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What are the brand perceptions among constituents? How does brand compare against competitors? Which social media channels are being used for discussion?
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European Union. This final decision impacted politics within the United Kingdom, and sent ripples across political and economic institutions worldwide.
601:: analytical information is complex and difficult to assimilate, thus, the goal of visualization is to understand and make sense of the information.
382:(drawings, simple sketches, or photographs), Audio (audio recordings of books, articles, talks, or discussions), or Videos (recording, live streams).
817:
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Lu, Y., Wang, F., & Maciejewski, R. (January 01, 2014). "Business intelligence from social media: a study from the VAST Box Office Challenge".
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have been representative cases that show the arising dangers of linking social media mining and politics. This has raised the question of
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Saboo, A. R., Kumar, V., & Ramani, G. (September 01, 2016). "Evaluating the impact of social media activities on human brand sales".
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Santos, Tiago; Louçã, and Helder Coelho, Jorge; Coelho, Helder (19 February 2020). "The Digital Transformation of the Public Sphere".
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1416:"Estimating deception in consumer reviews based on extreme terms: Comparison analysis of open vs. closed hotel reservation platforms"
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What is the relationship between business-relevant topics and issues? What are the causes for expressed intent (buy, churn etc.)?
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Type of Content: The content of data could be Text (written text that is easy to read and understand if you know the language),
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is the set of activities that assist in transforming raw data into insight, which in turn leads to a new base of knowledge and
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Which segments to target for acquisition, growth or retention? Who are the advocates and influences for the brand or product?
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Fan, W., & Gordon, M. D. (June 01, 2014). "The Power of Social Media Analytics". Association for Computing Machinery.
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Hanna, Mina; Isaak, Jim (14 August 2018). "User Data Privacy: Facebook, Cambridge Analytica, and Privacy Protection".
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Christopher Wylie speaks at a protest in Parliament Square following the Cambridge Analytica and Facebook data scandal
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of Data: The velocity of data in social media can be divided into two categories: data at rest and data in motion.
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in the political arena were revealed in the late 2010s. In particular, the involvement of the data mining company
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What are the new or emerging business-relevant topics or themes? Are new communities of influence emerging?
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content is getting generated in a variety of venues such as news sites and social networking sites (e.g.
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1230:"Social Media Analytics as a Business Intelligence Practice:Current Landscape & Future Prospects"
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397:). Depending on the type of project the data is collected for, the venue becomes very significant.
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This article is about the quantitative analysis of social media. For theoretical foundations, see
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Social media Analytics: Techniques and insights for Extracting Business Value Out of Social Media
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Social media Analytics: Techniques and insights for Extracting Business Value Out of Social Media
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which words or themes are important and if certain words relate to the topic we are exploring.
351:- so that its elements can be made addressable for more effective processing and analysis. The
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of velocity of data in motion can answer questions such as: How the sentiment of the general
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1463:"Framework for Social Media Analytics based on Multi-Criteria Decision Making (MCDM) model"
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1826:"Data firm broke Canadian privacy laws with involvement in Brexit, U.S. campaigns - probe"
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in the data? These are the important questions to be addressed before collecting data.
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Time: It is important to collect data posted in the time frame that is being analyzed.
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1714:"AggregateIQ Created Cambridge Analytica's Election Software, and Here's the Proof"
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social ideology and social networks, which in turn affects business intelligence.
535:, which is a private social network used to assist communication within business.
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The best visualizations are ones that expose something new about the underlying
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The references used may be made clearer with a different or consistent style of
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A buzz graph for the term "teszt" on Twitter in a social media monitoring tool.
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Recent research on social media analytics has emphasized the need to adopt a
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There are three main steps in analyzing social media: data identification,
1366:"Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud"
1685:"Cambridge Analytica did not misuse data in EU referendum, says watchdog"
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1656:"Cambridge Analytica 'not involved' in Brexit referendum, says watchdog"
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The taxonomy and the insight derived from that analysis are as follows:
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1744:"Investigation into the use of data analytics in political campaigns"
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780:. Statements consisting only of original research should be removed.
1545:"What can we learn from the Facebook—Cambridge Analytica scandal?"
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Conversation reach, velocity, share of voice, audience engagement
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Which competitor is gathering the most mentions in the context of
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Process of gathering and analyzing data from social media networks
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attempts to overturn the 2020 United States presidential election
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of Data: Is the data private or publicly available? Is there any
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1797:"Facebook suspends Canadian firm AggregateIQ over data scandal"
1234:
Journal of Internet Social Networking & Virtual Communities
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The possibilities of the dangers of social media analytics and
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Attributes of data that need to be considered are as follows:
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36:
1414:
Moon, Sangkil; Kim, Moon-Yong; Bergey, Paul K. (2019-09-01).
371:, we would want to make sure that the data collected is from
2027:
List of virtual communities with more than 1 million users
1306:"Social Media Analytics and Its Applications in Marketing"
483:
How many people mentioned Knowledge (XXG) in their tweets?
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Investigation Commissioners Office (November 6, 2018).
1101:"What is structured data? - Definition from WhatIs.com"
769:
1205:"The Three Elements of Successful Data Visualizations"
850:
2020 United States Presidential Election Controversies
355:, unlike structured data, is the least formatted data.
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Data identification is the process of identifying the
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Social network analysis, natural language processing
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is the process of gathering and analyzing data from
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2012:
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572:of the information is the answer to this question.
343:is a data that has been organized into a formatted
108:. Unsourced material may be challenged and removed.
1461:Muruganantham A.; Gandhi, G. Meera (2020-02-01).
1364:Luca, Michael; Zervas, Georgios (2016-12-01).
1292:International Journal of Research in Marketing
1895:
1304:Moon, Sangkil; Iacobucci, Dawn (2022-02-28).
605:helps the viewer gain insight from the data.
291:. A part of social media analytics is called
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627:Common use-cases for social media analytics
912:. Unsourced material may be challenged and
490:had the highest number of likes during the
71:Learn how and when to remove these messages
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1888:
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1855:"Results and turnout at the EU referendum"
1124:Ganis, Matthew; Kohirkar, Avinash (2015).
1075:Ganis, Matthew; Kohirkar, Avinash (2015).
1049:: CS1 maint: location missing publisher (
1017:Sponder, Marshall; Khan, Gohar F. (2017).
568:it could be used in good decision making?
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1128:. New York: IBM Press. pp. 247–248.
932:Learn how and when to remove this message
796:Learn how and when to remove this message
453:over the data; we need a way to tell the
244:Learn how and when to remove this message
226:Learn how and when to remove this message
168:Learn how and when to remove this message
1079:. New York: IBM Press. pp. 40–137.
833:2016 United States Presidential Election
818:2016 United States presidential election
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1578:Systems Research and Behavioral Science
1279:IEEE Computer Graphics and Applications
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1778:
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1151:"Enterprise Social Networks Explained"
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1836:from the original on January 13, 2020
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652:Active advocates, advocate influence
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1666:from the original on October 7, 2020
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910:adding citations to reliable sources
106:adding citations to reliable sources
1756:from the original on August 9, 2022
1724:from the original on April 25, 2018
1310:Foundations and Trends in Marketing
677:Social media exposure & impact
657:Social media information discovery
641:Social media audience segmentation
2022:List of social networking services
1865:from the original on April 1, 2021
1807:from the original on April 7, 2018
1695:from the original on June 13, 2022
707:Interests or preferences (theme),
633:Social media analytics techniques
25:
1467:Multimedia Tools and Applications
860:2021 United States Capitol attack
691:Social media behavior inferences
636:Social media performance metrics
531:. Internal social media includes
52:This article has multiple issues.
2213:Social network analysis software
2135:Virtual collective consciousness
1562:10.1111/j.1740-9713.2018.01139.x
1176:"Why data visualization matters"
882:
750:
718:Impacts on business intelligence
506:Machine Capacity: The amount of
438:, sentiment drivers, geography,
184:
82:
41:
2065:Organizational network analysis
1396:from the original on 2022-12-11
1346:from the original on 2022-12-11
1019:Digital analytics for marketing
93:needs additional citations for
60:or discuss these issues on the
1859:www.electoralcommission.org.uk
742:Role in international politics
672:Topic trends, sentiment ratio
418:Social media analytics process
1:
2484:Social information processing
2004:Personal knowledge networking
1543:Tarran, Brian (29 May 2018).
1432:10.1016/j.jbusres.2019.05.016
697:Natural language processing,
2075:Social aspects of television
1989:Enterprise social networking
1420:Journal of Business Research
1174:Steele, Julie (2012-02-15).
2218:Social networking potential
2100:Social media and psychology
1149:Kitt, Denise (2012-05-24).
776:the claims made and adding
664:Natural language processing
2510:
2305:Algorithmic radicalization
1994:Enterprise social software
1977:Distributed social network
711:, topic affinity matrices
630:Required business insight
612:
563:Information interpretation
472:Depth of Analysis: Simple
26:
2365:Six degrees of separation
2178:Collaborative consumption
2115:Social media optimization
2105:Social media intelligence
1642:Retrieved 8 January 2022.
1638:October 22, 2021, at the
1622:Retrieved 8 January 2021.
1479:10.1007/s11042-019-7470-2
1266:Communications of the ACM
993:Social media intelligence
615:information visualization
533:enterprise social network
2390:Suicide and the Internet
2375:Social media and suicide
1618:January 9, 2022, at the
968:Social media measurement
668:complex event processing
570:Visualization (graphics)
117:"Social media analytics"
2479:Social media management
2335:Friending and following
2325:Consequential strangers
2120:Social network analysis
1633:Washington Post website
1522:10.1109/MC.2018.3191268
1209:Harvard Business Review
998:Social network analysis
648:Social network analysis
293:social media monitoring
269:social media monitoring
29:social network analysis
2370:Social media addiction
2208:Social media analytics
2095:Social identity theory
2090:Social exchange theory
2085:Social data revolution
2070:Small-world experiment
1972:Corporate social media
1777:Cite journal requires
1382:10.1287/mnsc.2015.2304
870:
846:the media’s message.”
587:: before building the
474:descriptive statistics
419:
265:Social media analytics
261:
2489:Mass media monitoring
2310:Community recognition
2254:Collaborative finance
2188:Lateral communication
1999:Mobile social network
868:
724:business intelligence
417:
259:
18:Social media analysis
2416:Friendship recession
2355:Information overload
2264:Influencer marketing
2153:Account verification
2060:Interpersonal bridge
2055:Attention inequality
906:improve this section
102:improve this article
2385:Social network game
2380:Social invisibility
2228:Structural cohesion
2173:Collaboration graph
2130:Structural endogamy
2110:Social media mining
1613:Defense One website
1243:10.5171/2014.920553
1228:Ruhi, Umar (2014).
988:Social media mining
814:Cambridge Analytica
810:social media mining
315:Data identification
2494:Types of analytics
2350:Internet addiction
2345:Influence-for-hire
2340:Friendship paradox
2330:Friend of a friend
2320:Computer addiction
2183:Giant Global Graph
2050:Assortative mixing
1370:Management Science
1323:10.1561/1700000073
973:Sentiment analysis
951:2016 EU referendum
871:
761:possibly contains
510:needed to process
420:
262:
2466:
2465:
2458:Virtual community
2315:Complex contagion
2249:Attention economy
2223:Social television
2193:Reputation system
2045:Ambient awareness
1590:10.1002/sres.2644
1376:(12): 3412–3427.
1294:, 33, 3, 524-541.
1135:978-0-13-389256-7
1086:978-0-13-389256-7
978:Ambient awareness
946:Brexit referendum
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353:unstructured data
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16:(Redirected from
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2400:Viral phenomenon
2163:Change detection
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1861:. 2019-09-25.
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1803:. 2018-04-07.
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891:This section
889:
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764:
759:This section
757:
748:
747:
741:
739:
735:
733:
732:data breaches
727:
725:
717:
710:
706:
704:
700:
696:
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689:
685:
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619:
616:
608:
607:Visualization
604:
600:
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589:visualization
586:
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466:
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458:
456:
452:
448:
445:Developing a
443:
441:
437:
433:
429:
428:Data analysis
423:Data analysis
422:
416:
409:
405:
402:
399:
396:
392:
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314:
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310:
309:data analysis
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278:
274:
270:
266:
258:
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245:
230:
227:
219:
216:February 2015
209:
205:
199:
198:
193:This article
191:
182:
181:
172:
169:
161:
158:February 2015
150:
147:
143:
140:
136:
133:
129:
126:
122:
119: –
118:
114:
113:Find sources:
107:
103:
97:
96:
91:This article
89:
85:
80:
79:
74:
72:
65:
64:
59:
58:
53:
48:
39:
38:
30:
19:
2431:User profile
2207:
2203:Social graph
2036:Concepts and
1981:
1934:Professional
1915:social media
1867:. Retrieved
1858:
1849:
1838:. Retrieved
1829:
1820:
1809:. Retrieved
1800:
1791:
1770:cite journal
1758:. Retrieved
1737:
1726:. Retrieved
1717:
1708:
1697:. Retrieved
1689:the Guardian
1688:
1679:
1668:. Retrieved
1659:
1627:
1606:
1581:
1577:
1571:
1552:
1549:Significance
1548:
1538:
1516:(8): 56–59.
1513:
1509:
1503:
1470:
1466:
1456:
1423:
1419:
1409:
1398:. Retrieved
1373:
1369:
1359:
1348:. Retrieved
1313:
1309:
1299:
1291:
1286:
1278:
1273:
1268:, 57, 6, 74.
1265:
1260:
1233:
1223:
1212:. Retrieved
1208:
1183:. Retrieved
1179:
1169:
1158:. Retrieved
1154:
1144:
1125:
1119:
1108:. Retrieved
1104:
1095:
1076:
1018:
1012:
943:
928:
919:
904:Please help
892:
853:
844:
836:
826:data privacy
807:
792:
783:
760:
736:
728:
721:
709:correlations
603:Storytelling
599:Tell a story
574:
566:
467:
459:
444:
440:demographics
426:
387:Social media
331:
318:
306:
296:
292:
268:
264:
263:
240:
222:
213:
194:
164:
155:
145:
138:
131:
124:
112:
100:Please help
95:verification
92:
68:
61:
55:
54:Please help
51:
2426:Researchers
2158:Aggregation
983:Data mining
703:data mining
2473:Categories
2360:Overchoice
2198:Social bot
2144:Models and
2125:Social web
1954:Adolescent
1869:2022-10-23
1840:2022-06-13
1811:2022-06-13
1728:2022-06-13
1699:2022-06-13
1670:2022-06-13
1584:(6): 778.
1555:(3): 4–5.
1400:2022-12-11
1350:2022-12-11
1214:2016-12-11
1185:2016-12-11
1160:2016-11-05
1155:CRM Switch
1110:2016-12-06
1105:WhatIs.com
1004:References
770:improve it
699:clustering
620:Techniques
613:See also:
547:population
543:Dimensions
488:politician
463:word cloud
447:data model
345:repository
208:footnoting
128:newspapers
57:improve it
2298:Phenomena
2237:Economics
2146:processes
1598:210550036
1495:254869748
1487:1573-7721
1448:181482746
1440:0148-2963
1426:: 83–96.
1390:0025-1909
1340:247201448
1332:1555-0753
1281:, 34, 5.)
1252:167544401
1045:cite book
1037:975370877
893:does not
774:verifying
512:data sets
476:based on
436:sentiment
408:copyright
404:Ownership
337:Structure
281:Instagram
63:talk page
2038:theories
2014:Services
1965:Networks
1929:Personal
1863:Archived
1834:Archived
1805:Archived
1760:June 13,
1751:Archived
1722:Archived
1693:Archived
1664:Archived
1660:BBC News
1636:Archived
1616:Archived
1530:52047339
1394:Archived
1344:Archived
1236:: 1–12.
962:See also
858:and the
585:audience
577:patterns
539:Velocity
529:LinkedIn
525:Facebook
455:computer
391:Facebook
359:Language
349:database
285:LinkedIn
277:Facebook
275:such as
204:citation
2441:Persona
1830:Reuters
1749:: 116.
1718:Gizmodo
949:in the
914:removed
899:sources
839:scandal
816:in the
768:Please
595:viewer.
521:Twitter
395:Twitter
385:Venue:
321:subsets
303:Process
289:Twitter
142:scholar
1949:Clique
1939:Sexual
1596:
1528:
1493:
1485:
1446:
1438:
1388:
1338:
1330:
1250:
1132:
1083:
1035:
1025:
874:Brexit
822:Brexit
556:system
492:debate
486:Which
380:Photos
365:Region
326:wisdom
144:
137:
130:
123:
115:
2395:Tribe
1944:Value
1922:Types
1754:(PDF)
1747:(PDF)
1594:S2CID
1526:S2CID
1491:S2CID
1444:S2CID
1336:S2CID
1248:S2CID
375:only.
373:India
369:India
287:, or
149:JSTOR
135:books
1982:list
1913:and
1801:CNBC
1783:help
1762:2022
1510:IEEE
1483:ISSN
1436:ISSN
1386:ISSN
1328:ISSN
1130:ISBN
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