Basics of qualitative research: Grounded theory procedures and techniques

This paper addresses the scope for more integrated general analysis of diversity in science, technology and society. It proposes a framework recognizing three necessary but individually insufficient properties of diversity. Based on 10 quality criteria, it suggests a general quantitative non-parametric diversity heuristic. This allows the systematic exploration of diversity under different perspectives, including divergent conceptions of relevant attributes and contrasting weightings on different diversity properties. It is shown how this heuristic may be used to explore different possible trade-offs between diversity and other aspects of interest, including portfolio interactions. The resulting approach offers a way to be more systematic and transparent in the treatment of scientific and technological diversity in a range of fields, including conservation management, research governance, energy policy and sustainable innovation.

A General Framework for Analysing Diversity in Science, Technology and Society

The American political science review

The Nature of Prejudice

Several messages express opinions about events, products, and services, political views or even their author's emotional state and mood. Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services, and simply to better understand aspects of social communication in Online Social Networks (OSNs). There are multiple methods for measuring sentiments, including lexical-based approaches and supervised machine learning methods. Despite the wide use and popularity of some methods, it is unclear which method is better for identifying the polarity (i.e., positive or negative) of a message as the current literature does not provide a method of comparison among existing methods. Such a comparison is crucial for understanding the potential limitations, advantages, and disadvantages of popular methods in analyzing the content of OSNs messages. Our study aims at filling this gap by presenting comparisons of eight popular sentiment analysis methods in terms of coverage (i.e., the fraction of messages whose sentiment is identified) and agreement (i.e., the fraction of identified sentiments that are in tune with ground truth). We develop a new method that combines existing approaches, providing the best coverage results and competitive agreement. We also present a free Web service called iFeel, which provides an open API for accessing and comparing results across different sentiment methods for a given text.

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Comparing and combining sentiment analysis methods

Emoticons are a key aspect of text-based communication, and are the equivalent of nonverbal cues to the medium of online chat, forums, and social media like Twitter. As emoticons become more widespread in computer mediated communication, a vocabulary of different symbols with subtle emotional distinctions emerges especially across different cultures. In this paper, we investigate the semantic, cultural, and social aspects of emoticon usage on Twitter and show that emoticons are not limited to conveying a specific emotion or used as jokes, but rather are socio-cultural norms, whose meaning can vary depending on the identity of the speaker. We also demonstrate how these norms propagate through the Twitter @-reply network. We confirm our results on a large-scale dataset of over one billion Tweets from different time periods and countries.

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Emoticon Style: Interpreting Differences in Emoticons Across Cultures

Relying on Gudykunst's cultural variability in communication (CVC) framework and culture-specific facial expressions of emotion, we examined how people's use of emoticons varies cross-culturally. By merging emoticon usage patterns on Twitter with Hofstede's national culture scores and national indicators across 78 countries, this study found that people within individualistic cultures favor horizontal and mouth-oriented emoticons like :), while those within collectivistic cultures favor vertical and eye-oriented emoticons like ^_^. Our study serves to demonstrate how recent big data-driven approaches can be used to test research hypotheses in cross-cultural communication effectively from the methodological triangulation perspective. Implications and limitations regarding the findings of this study are also discussed.

Cross-Cultural Comparison of Nonverbal Cues in Emoticons on Twitter: Evidence from Big Data Analysis

Social media has become prominently popular. Tens of millions of users login to social media sites like Twitter to disseminate breaking news and share their opinions and thoughts. For businesses, social media is potentially useful for monitoring the public perception and the social reputation of companies and products. Despite great potential, how bad news about a company influences the public sentiments in social media has not been studied in depth. The aim of this study is to assess people’s sentiments in Twitter upon the spread of two types of information: corporate bad news and a CEO’s apology. We attempted to understand how sentiments on corporate bad news propagate in Twitter and whether any social network feature facilitates its spread. We investigated the Domino’s Pizza crisis in 2009, where bad news spread rapidly through social media followed by an official apology from the company. Our work shows that bad news spreads faster than other types of information, such as an apology, and sparks a great degree of negative sentiments in the network. However, when users converse about bad news repeatedly, their negative sentiments are softened. We discuss various reactions of users towards the bad news in social media such as negative purchase intent.

Managing Bad News in Social Media: A Case Study on Domino’s Pizza Crisis

Video-sharing social media like YouTube provide access to diverse cultural products from all over the world, making it possible to test theories that the Web facilitates global cultural convergence. Drawing on a daily listing of YouTube's most popular videos across 58 countries, we investigate the consumption of popular videos in countries that differ in cultural values, language, gross domestic product, and Internet penetration rate. Although online social media facilitate global access to cultural products, we find this technological capability does not result in universal cultural convergence. Instead, consumption of popular videos in culturally different countries appears to be constrained by cultural values. Cross-cultural convergence is more advanced in cosmopolitan countries with cultural values that favor individualism and power inequality.

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Cultural values and cross-cultural video consumption on YouTube.

While social media has become an important platform for social reputation, the emotional responses of users toward bad news have not been investigated thoroughly. We analyzed a total of 20,773 Twitter messages by 15,513 users to assess the influence of bad news and public apology in social media. Based on both computerized, quantitative sentiment analysis and in-depth qualitative analysis, we found that rapid public apology effectively and immediately reduced the level of negative sentiment, where the degree of change in sentiments differed by the type of interactions users engaged in. The majority of users who directly conversed with corporate representatives on the new media were not typical consumers, but experts and practitioners. We extend the existing cognitive model and suggest the audiences’ psychological reaction model to describe the information processing process during and after an organizational crisis and response. We also discuss various measures through which companies can respond to a crisis properly in social media in a fashion that is different from conventional mass media.

/pdf/the-effect-of-bad-news-and-ceo-apology-of-corporate-on-user-3gwif7vv6x.pdf

Jaram Park

Papers

Emoticon Style: Interpreting Differences in Emoticons Across Cultures

Cross-Cultural Comparison of Nonverbal Cues in Emoticons on Twitter: Evidence from Big Data Analysis

Managing Bad News in Social Media: A Case Study on Domino’s Pizza Crisis

Cultural values and cross-cultural video consumption on YouTube.

The Effect of Bad News and CEO Apology of Corporate on User Responses in Social Media.