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The Discourse of Online Reviews is the first book to provide an account of the discursive, pragmatic and rhetorical features of this rapidly growing form of technologically-mediated communication. Examining a corpus of over 1,000 consumer reviews, Camilla Vásquez explores many of the discourse features that are characteristic of this new, user-generated, computer-mediated and primarily text-based genre. She investigates the language used by reviewers as they forge connections with their audiences to draw them into their stories, as they construct their expertise and authority on various subjects and as they evaluate and assess their consumer experiences. She also demonstrates how reviewers display their awareness about emerging conventions of the very genre in which they are participating. This book adopts an eclectic approach to the analysis of discourse, and explores topics such as evaluation, identity and intertextuality as they occur in online reviews of hotels, restaurants, recipes, films and other consumer products.

When students answer an in-class conceptual question individually using clickers, discuss it with their neighbors, and then revote on the same question, the percentage of correct answers typically increases. This outcome could result from gains in understanding during discussion, or simply from peer influence of knowledgeable students on their neighbors. To distinguish between these alternatives in an undergraduate genetics course, we followed the above exercise with a second, similar (isomorphic) question on the same concept that students answered individually. Our results indicate that peer discussion enhances understanding, even when none of the students in a discussion group originally knows the correct answer.

Facebook and Conversation Analysis investigates the structure and organization of comments on a major social media platform, Facebook, using applied conversation analysis methods. Providing previously undocumented insights into the structure of comment threads, this book demonstrates that they have a meaningful organization, rather than casually following one another. Although normally used to explore the structure of spoken conversations, in recent years conversation analysis approaches have been successfully applied to examine online interactions on Twitter, discussion forums and email exchanges. By turning this approach towards Facebook comments, Matteo Farina provides clear and important insights into the organization of this type of social interaction. Supported by a large sample of data, with findings based on a corpus of 213 comment threads, with over 1,200 comments exchanged by 266 contributors, this book makes an important contribution to our understanding of the way people communicate on Facebook.

Metaphor has long provided a rich way to speak about the unspeakable, to refer to delicate issues.Sex is one such area.This book follows a cognitive-linguistic and relevance-theoretic approach to the language of sex, considering metaphor as a bridge that brings together mind and language.

Infodemiology can be defined as the science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy. Infodemiology data can be collected and analyzed in near real time. Examples for infodemiology applications include the analysis of queries from Internet search engines to predict disease outbreaks (eg. influenza), monitoring peoples' status updates on microblogs such as Twitter for syndromic surveillance, detecting and quantifying disparities in health information availability, identifying and monitoring of public health relevant publications on the Internet (eg. anti-vaccination sites, but also news articles or expert-curated outbreak reports), automated tools to measure information diffusion and knowledge translation, and tracking the effectiveness of health marketing campaigns. Moreover, analyzing how people search and navigate the Internet for health-related information, as well as how they communicate and share this information, can provide valuable insights into health-related behavior of populations. Seven years after the infodemiology concept was first introduced, this paper revisits the emerging fields of infodemiology and infoveillance and proposes an expanded framework, introducing some basic metrics such as information prevalence, concept occurrence ratios, and information incidence. The framework distinguishes supply-based applications (analyzing what is being published on the Internet, eg. on Web sites, newsgroups, blogs, microblogs and social media) from demand-based methods (search and navigation behavior), and further distinguishes passive from active infoveillance methods. Infodemiology metrics follow population health relevant events or predict them. Thus, these metrics and methods are potentially useful for public health practice and research, and should be further developed and standardized.

Managers are very interested in word-of-mouth communication because they believe that a product's success is related to the word of mouth that it generates. However, there are at least three significant challenges associated with measuring word of mouth. First, how does one gather the data? Because the information is exchanged in private conversations, direct observation traditionally has been difficult. Second, what aspect of these conversations should one measure? The third challenge comes from the fact that word of mouth is not exogenous. While the mapping from word of mouth to future sales is of great interest to the firm, we must also recognize that word of mouth is an outcome of past sales. Our primary objective is to address these challenges. As a context for our study, we have chosen new television (TV) shows during the 1999–2000 seasons. Our source of word-of-mouth conversations is Usenet, a collection of thousands of newsgroups with diverse topics. We find that online conversations may offer an easy and cost-effective opportunity to measure word of mouth. We show that a measure of the dispersion of conversations across communities has explanatory power in a dynamic model of TV ratings.

Text classification is an essential aspect in many applications, such as spam detection and sentiment analysis. With the growing number of textual documents and datasets generated through social media and news articles, an increasing number of machine learning methods are required for accurate textual classification. For this paper, a comprehensive evaluation of the performance of multiple supervised learning models, such as logistic regression (LR), decision trees (DT), support vector machine (SVM), AdaBoost (AB), random forest (RF), multinomial naive Bayes (NB), multilayer perceptrons (MLP), and gradient boosting (GB), was conducted to assess the efficiency and robustness, as well as limitations, of these models on the classification of textual data. SVM, LR, and MLP had better performance in general, with SVM being the best, while DT and AB had much lower accuracies amongst all the tested models. Further exploration on the use of different SVM kernels was performed, demonstrating the advantage of using linear kernels over polynomial, sigmoid, and radial basis function kernels for text classification. The effects of removing stop words on model performance was also investigated; DT performed better with stop words removed, while all other models were relatively unaffected by the presence or absence of stop words.

The author develops \"netnography\" as an online marketing research technique for providing consumer insight. Netnography is ethnography adapted to the study of online communities. As a method, netnography is faster, simpler, and less expensive than traditional ethnography and more naturalistic and unobtrusive than focus groups or interviews. It provides information on the symbolism, meanings, and consumption patterns of online consumer groups. The author provides guidelines that acknowledge the online environment, respect the inherent flexibility and openness of ethnography, and provide rigor and ethics in the conduct of marketing research. As an illustrative example, the author provides a netnography of an online coffee newsgroup and discusses its marketing implications.

Online spaces that enable shared public interpersonal communications are of significant social, organizational, and economic importance. In this paper, a theoretical model and associated unobtrusive method are proposed for researching the relationship between online spaces and the behavior they host. The model focuses on the collective impact that individual information-overload coping strategies have on the dynamics of open, interactive public online group discourse. Empirical research was undertaken to assess the validity of both the method and the model, based on the analysis of over 2.65 million postings to 600 Usenet newsgroups over a 6-month period. Our findings support the assertion that individual strategies for coping with \"information overload\" have an observable impact on large-scale online group discourse. Evidence was found for the hypotheses that: (1) users are more likely to respond to simpler messages in overloaded mass interaction; (2) users are more likely to end active participation as the overloading of mass interaction increases; and (3) users are more likely to generate simpler responses as the overloading of mass interaction grows. The theoretical model outlined offers insight into aspects of computer-mediated communication tool usability, technology design, and provides a road map for future empirical research.

Public opinion research demonstrates that citizens' opinions depend on elite rhetoric and interpersonal conversations. Yet, we continue to have little idea about how these two forces interact with one another. In this article, we address this issue by experimentally examining how interpersonal conversations affect (prior) elite framing effects. We find that conversations that include only common perspectives have no effect on elite framing, but conversations that include conflicting perspectives eliminate elite framing effects. We also introduce a new individual level moderator of framing effects-called \"need to evaluate\"-and we show that framing effects, in general, tend to be short-lived phenomena. In the end, we clarify when elites can and cannot use framing to influence public opinion and how interpersonal conversations affect this process.