Bias reduction in the population size estimation of large data sets
کاهش تمایل در برآورد اندازه جمعیت مجموعه داده های بزرگ-2020
Estimation of the population size of large data sets and hard to reach populations can be a significant problem. For example, in the military, manpower is limited and the manual processing of large data sets can be time consuming. In addition, accessing the full population of data may be restricted by factors such as cost, time, and safety. Four new population size estimators are proposed, as extensions of existing methods, and their performances are compared in terms of bias with two existing methods in the big data literature. These would be particularly beneficial in the context of time-critical decisions or actions. The comparison is based on a simulation study and the application to five real network data sets (Twitter, LiveJournal, Pokec, Youtube, Wikipedia Talk). Whilst no single estimator (out of the four proposed) generates the most accurate estimates overall, the proposed estimators are shown to produce more accurate population size estimates for small sample sizes, but in some cases show more variability than existing estimators in the literature.
Keywords: Relative bias | Twitter | Size estimator | Youtube | Random walk sampling
TUTORIAL: AI research without coding: The art of fighting without fighting: Data science for qualitative researchers
آموزش: تحقیقات هوش مصنوعی بدون رمزگذاری: هنر مبارزه بدون جنگ: علم داده برای محققان کیفی-2020
In this tutorial, we show how to scrape and collect online data, perform sentiment analysis, social network analysis, tribe finding, and Wikidata cross-checks, all without using a single line of programming code. In a stepby- step example, we use self-collected data to perform several analyses of the glass ceiling. Our tutorial can serve as a standalone introduction to data science for qualitative researchers and business researchers, who have avoided learning to program. It should also be useful for experienced data scientists who want to learn about the tools that will allow them to collect and analyze data more easily and effectively.
Keywords: Twitter | Data scraping | Sentiment analysis | Tribe finding | Wikidata
Geo-semantic-parsing: AI-powered geoparsing by traversing semantic knowledge graphs
تجزیه جغرافیایی-معنایی: تجزیه و تحلیل ژئوپارسی با هوش مصنوعی با عبور از نمودارهای دانش معنایی-2020
Online social networks convey rich information about geospatial facets of reality. However in most cases, geographic information is not explicit and structured, thus preventing its exploitation in real-time applications. We address this limitation by introducing a novel geoparsing and geotagging technique called Geo-Semantic- Parsing (GSP). GSP identifies location references in free text and extracts the corresponding geographic coordinates. To reach this goal, we employ a semantic annotator to identify relevant portions of the input text and to link them to the corresponding entity in a knowledge graph. Then, we devise and experiment with several efficient strategies for traversing the knowledge graph, thus expanding the available set of information for the geoparsing task. Finally, we exploit all available information for learning a regression model that selects the best entity with which to geotag the input text. We evaluate GSP on a well-known reference dataset including almost 10 k event-related tweets, achieving F1=0.66. We extensively compare our results with those of 2 baselines and 3 state-of-the-art geoparsing techniques, achieving the best performance. On the same dataset, competitors obtain F1 ≤ 0.55. We conclude by providing in-depth analyses of our results, showing that the overall superior performance of GSP is mainly due to a large improvement in recall, with respect to existing techniques.
Keywords: Geoparsing | Geotagging | Artificial intelligence | Knowledge graphs | Twitter
Can twitter analytics predict election outcome? An insight from 2017 Punjab assembly elections
آیا تحلیل های توییتر می توانند نتیجه انتخابات را پیش بینی کنند؟ بینشی از انتخابات مجلس پنجم 2017-2020
Since the beginning of this decade, there has seen an exponential growth in number of internet users using social media, especially Twitter for sharing their views on various topics of common interest like sports, products, politics etc. Due to the active participation of large number of people on Twitter, huge amount of data (i.e. big data) is being generated, which can be put to use (after refining) to analyze real world problems. This paper takes into consideration the Twitter data related to the 2017 Punjab (a state of India) assembly elections and applies different social media analytic techniques on collected tweets to extract and unearth hidden but useful information. In addition to this, we have employed machine learning algorithm to perform polarity analysis and have proposed a new seat forecasting method to accurately predict the number of seats that a political party is likely to win in the elections. Our results confirmed that Indian National Congress was likely to emerge winner and that in fact was the outcome, when results got declared.
Keywords: Analytics | Election prediction | Social media | Natural language processing | Machine learning | Sentiment analysis | Twitter
Digital social capital and performance of initial coin offerings
سرمایه اجتماعی دیجیتال و عملکرد ارائه سکه های اولیه-2020
The Initial Coin Offering (ICO) has emerged as an original way for companies to leverage funding. In this study, we analyze 537 companies that chose the ICO model in 2017 and investigate how their digital social capital is related to the rank of the ICO based on the market capitalization. The goal of this work is to better understand the role of digital social capital in ICO success. Multiple facets of digital presence are analyzed, such as website, ICO activity on social networks like Twitter, the community built and the activeness of the community. We apply an exploratory factor analysis to leverage the main factors that can be used as latent variables, and build an original research model. Structural equation modelling is used for model evaluation and hypothesis testing. Curvilinear analyses allow us to obtain a finer vision of our results. We also verify the robustness of our results in predicting the ICO rank further in time. Our results suggest that social capital is indicative of the ICO performance. The website audience is found to be the most predictive. However, the audience and the centrality of an ICO in the community seems less important than the activeness of an ICO and of the related community on social networks.
Keywords: Initial coin offering | Cryptocurrency | Digital presence | Public interest | Social networks | Website | Crowdfunding | Ewom | Marketing | Visibility
If I give you my emotion, what do I get? Conceptualizing and measuring the co-created emotional value of the brand
"اگر احساسات خود را به شما نشان دهم ، چه می توانم دریافت کنم؟" مفهوم سازی و اندازه گیری ارزش عاطفی ایجاد شده از برند-2020
The emotional value of interactions is a pillar construct in the brand value co-creation domain. So far, research has neglected the search for a measure adequately considering emotional-based joint interactions. Thanks to a netnographic sentiment analysis of 7605 brand-users’ interactions retrieved from 18 Twitter brand profiles, this paper advances knowledge on brand co-creation and introduces a new concept in the marketing domain, the cocreated emotional value of the brand, operationalised through the Emotional Co-Creation Score (ECCS). The paper reveals that different emotional experiential paths can be generated by the simultaneous interaction between the brand and its consumers. In particular, it shows that some sectors co-create more than others. Furthermore, brands provide more positive emotions than consumers and, when dealing with consumers’ extreme polar emotions, they compensate consumers’ emotions by calibrating the ECCS, which is not influenced by the frequency of Likes, and only marginally influenced by the frequency of interactions.
Keywords: Brand | Co-creation | Emotional value | Sentiment analysis | Brand measure | Marketing management
Eco-friendliness and fashion perceptual attributes of fashion brands: An analysis of consumers’ perceptions based on twitter data mining
سازگاری با محیط زیست و ویژگی های ادراکی مد برندهای مد: تحلیلی از درک مصرف کنندگان براساس داده کاوی توییتر-2020
This study explores if there is a convergence between the concepts of fashion and eco-friendliness in consumer perception of a fashion brand.We assume that increased eco-friendly perception will influence the brand image positively, with this impact being much higher for luxury than for high and fast fashion brands. The hypotheses are tested using data collected from Twitter. We analyzed the fashion clothing brands with the highest number of followers on the Socialbakers list and applied a novel social network mining methodology that allows measuring the relationship between each brand and two perceptual attributes (fashion and eco-friendliness). The method is based on attribute exemplarsdthat is, Twitter accounts that represent a perceptual attribute. Our exemplars catalyze social media conversations on fashion (identified in our research by the keywords “fashion,” “glamour,” and “style”) and ecofriendliness (keywords “environment” and “ethical business”). Based on social network analysis theory, we computed a similarity function between the followers of the exemplars and those of the brand. The results suggest that there is a correlation between the fashion and the eco-friendliness perceptual attributes of a brand; however, this correlation is far stronger for luxury brands than for high and fast fashion brands. The difference in the correlations confirms the recent tendency of fashion luxury brand to increasingly consider treating environmental issues as part of their core business and not just as added value to the brand’s offer.
Keywords: Fashion brands | Twitter | Consumer perception | Environment | Ethical business | Brand image | Big data
THE RELATIONSHIP BETWEEN JIM CROW LAWS AND SOCIAL CAPITAL FROM 1997-2014: A 3-LEVEL MULTILEVEL HIERARCHICAL ANALYSIS ACROSS TIME, COUNTY AND STATE
رابطه حقوقی بین JIM CROW و سرمایه های اجتماعی از سال 1997-2014: یک آنالیز چند سطحی سلسله مراتبی زمان و کشور-2020
Purpose: The purpose was to use Twitter to conduct online surveillance of negative sentiment towards Mexicans and Hispanics during the 2016 United States presidential election, and to examine its relationship with mental well-being in this targeted group at the population level. Methods: Tweets containing the terms Mexican(s) and Hispanic(s) were collected within a 20- week period of the 2016 United States presidential election (November 9th 2016). Sentiment analysis was used to capture percent negative tweets. A time series lag regression model was used to examine the association between percent count of negative tweets mentioning Mexicans and Hispanics and percent count of worry among Hispanic Gallup poll respondents. Results: Of 2,809,641 tweets containing terms Mexican(s) and Hispanic(s), 687,291 tweets were negative. Among 8,314 Hispanic Gallup respondents, a mean of 33.5% responded to be worried on a daily basis. A significant lead time of 1 week was observed, showing that negative tweets mentioning Mexicans and Hispanics appeared to forecast daily worry among Hispanics by 1 week. Conclusion: Surveillance of online negative sentiment towards racially vulnerable population groups can be captured using social media. This has potential to identify early warning signals for symptoms of mental well-being among targeted groups at the population level.
Keywords: social capital | Jim Crow laws | policy | segregation | social determinants of health | health disparities | health inequities
User engagement for mobile payment service providers : introducing the social media engagement model
تعامل کاربر برای ارائه دهندگان خدمات پرداخت تلفن همراه: معرفی مدل تعامل رسانه های اجتماعی-2020
Twitter is being used by mobile wallet firms for customer acquisition, relationship management, marketing and promotional purposes. This study examines service advertisement and promotional tweets by mobile wallet firms on Twitter. For this study, timeline data of top four mobile wallet firms of India, Paytm, MobiKwik, Freecharge and Oxigen Wallet were extracted from their Twitter screen (firm generated tweets). The user generated tweets were also extracted, using the search terms as firms name. This study proposes a Social Media Engagement model for understanding user dynamics. The study provides three interesting inputs for promotional marketing tweets, firstly, firm should post mix of the tweets with respect to content type (i.e. informational, entertainment, remuneration and social). Secondly, a periodic campaigning is needed by the firms; and lastly, firms should focus on increasing their network size. The implications of these findings can help firms managers and marketers in planning effective social media marketing campaigns.
Keywords: Social media marketing | Digital payments | Twitter analytics | Mobile wallets | Customer engagement
Discourses of exclusion on Twitter in the Turkish Context: #ülkemdesuriyeliistemiyorum (#idontwantsyriansinmycountry)
محرومیت گفتگوها در توییتر در متن ترکی: # ülkemdesuriyeliistemiyorum (#idontwantsyriansinmycountry)-2020
The new communicative affordances of online spaces have transformed the ways and domains we build and negotiate meaning. At the same time, they have introduced diverse channels to produce and disseminate animosity. This article explores online discourses as new communicative environments characterized by their unique textual and semiotic features to unfold the discursive constructions of hate and hostility towards Syrian refugees in Turkey. Building on the principles of the Social Media Critical Discourse Studies (SM-CDS) framework proposed by KhosraviNik (2018) and the Discourse-Historical Approach (DHA) by Reisigl and Wodak (2001), the study analyzes a subset of tweets that includes the hashtag #ülkemdesuriyeliistemiyorum (#idontwantsyriansinmycountry) to understand its functions in constructing and proliferating an exclusionary discourse against refugees. The study focuses on referential, argumentation and intensification strategies used in tweets as well as their wider socio-political implications. The results reveal that refugees in Turkey are delineated as threats, invaders, criminals and potential dangers by the users of online media. It is further observed that a sharper rhetoric and a more intense negative-other representation emerge in Twitter as an online public space compared to print media discourses. While scrutinizing the (re)construction and representation of refugees, our analysis has also uncovered that hate and hostility discourses towards refugees constantly operate to build a collective nationalist identity. This interlocking relationship between constructing refugees through stereotypical attributes as a homogeneously dangerous group and forming a collective Turkish identity is manifested at each level of our analysis
Keywords: Online hostility | Hashtag | Refugees | Critical discourse analysis | Turkey | Twitter