Self-Aware Contextual Behavior Analysis for Service Quality Assurance Over Social Networks

Social media allows people to share their ideologue through an efficient channel of communication. The social dialogues carry sentiment in expression regarding a particular social profile, trend, or topic. In our research, we have collected real-time user comments and feedbacks from Twitter portals of two food delivery services. This is followed by the extraction of the most prevalent contexts using natural language analytics. Further, our proposed algorithmic framework is used to generate a signed social network to analyze the product-centric behavioral sentiment. Analysis of sentiment with the fine-grained level about contexts gave a broader view to evaluate and perform contextual predictions. Customer behavior is analyzed, and the outcome is received in terms of positive and negative contexts. The results from our social behavioral model predicted the positive and negative contextual sentiments of customers, which can be further used to help in deciding future strategies and assuring service quality for better customer satisfaction.

Self-Aware Contextual Behavior Analysis for Service Quality Assurance over Social Networks

Social media allows people to share their ideologue through an efficient channel of communication. The social dialogues carry sentiment in expression regarding a particular social profile, trend, or topic. In our research, we have collected real-time user comments and feedbacks from Twitter portals of two food delivery services. This is followed by the extraction of the most prevalent contexts using natural language analytics. Further, our proposed algorithmic framework is used to generate a signed social network to analyze the product-centric behavioral sentiment. Analysis of sentiment with the fine-grained level about contexts gave a broader view to evaluate and perform contextual predictions. Customer behavior is analyzed, and the outcome is received in terms of positive and negative contexts. The results from our social behavioral model predicted the positive and negative contextual sentiments of customers, which can be further used to help in deciding future strategies and assuring service quality for better customer satisfaction.

Salient deliberative norm types in comment sections on news sites

Civil and argumentative public discussions are considered crucial for functioning democracies. Among other factors, the quality of user discussions of political issues on news sites depends on prevalent discussion norms. We integrate injunctive and descriptive norms into news comment research, assuming that the degree of salience of the respective norm influences the commenting behavior. Furthermore, we discuss how technical affordances such as default comment sorting determine the comment visibility and thus the salience of norms. Using data from a content analysis of 8162 comments on eight German news sites, we investigate how the two norm types influence deliberative forms of commenting. The results show that different types of salient injunctive and descriptive norms promote norm-compliant commenting. Furthermore, the default comment sorting can determine which comments are more or less salient. The results underline the importance of distinguishing different norm types in analyzing the quality of user comments in comment sections.

Twitter Engagement in Media Organizations: The Case of the Greek National Broadcasting Corporation

A key parameter in the strategy of news organizations remains the exploitation of factors (such as post time and post type) that enhance the engagement level within online communities on social media. The purpose of this paper is to examine the relationship between post time and post type in correlation with audience response in the Twitter digital platform. Specifically, the study aims to ascertain how the two specific variables affect user engagement with its Twitter posts and how they shape the effectiveness of communication on social networks. The analysis includes 7.122 tweets of the Greek National Broadcasting Corporation (ERT) over four months. Moreover, the study analyzes the tone of user comments on the Twitter posts of the specific public media organizations to understand in-depth how the users communicate their views publicly. The collection of comments lasted seven weeks and they numbered 265 in 2639 tweets. Regarding the post time variable, the study came to important findings on user behavior during the 24 h, as the number of Retweets appears to increase in the morning compared to the afternoon. It was also found that as time goes on, the user is interested in leaving his personal opinion. Regarding the correlation of post type with user engagement, it was found that the accompaniment of a tweet with audiovisual material has a tempting effect on users.

Sentiment Analysis and Summarization of Facebook Posts on News Media

Social media has become part of daily life in the modern world. News media companies (NMC) use social network sites including Facebook pages to let net users keep updated. Public expression is important to NMC for making valuable journals, but it is not cost-effective to collect millions of feedback by human effort, which can instead be automated by sentiment analysis. This chapter presents a mobile application called Facemarize that summarizes the contents of news media Facebook pages using sentiment analysis. The sentiment of user comments can be quickly analyzed and summarized with emotion detection. The sentiment analysis achieves an accuracy of over 80%. In a survey with 30 participants including journalists, journalism students, and journalism graduates, the application gets at least 4.9 marks (in a 7-point Likert scale) on the usefulness, ease of use, ease of learning, and satisfaction with a mean reliability score of 3.9 (out of 5), showing the effectiveness of the application.

Deep Learning Approach of Product Evaluation Using Comment Analysis

Digital reviews now play a critical role in strengthening global consumer communications and influencing consumer purchasing patterns. Consumers can use e-commerce giants like Amazon, Flipchart, Snap deal, Jio and others to share their experiences and provide real insights about the performance of a product to future buyers. The classification of reviews into positive and negative sentiment is required in order to derive relevant insights from a big set of reviews. Comment Analysis is a computer programme that extracts subjective data from text. Out of Various Classification models Deep Learning Approach of Product Evaluation Using Comment Analysis is to develop a model that uses AI technologies like Deep Learning to process thousands and millions of online reviews on a product in a split second of time and rate the products on a scale of 1-5 based on the user comments We have worked on two deep learning models based on Recurrent Neural Networks (RNN) and Graph Convolution Network (GCN). Keywords: LSTN, GCN, NLTK

Stereotypes and sexism? Effects of gender, topic, and user comments on journalists’ credibility

Gendered social roles raise assumptions about what female and male journalists ought to do. Prior studies have suggested that covering counter-stereotypical topics may decrease journalists’ source and their work’s message credibility. Pertaining also to prior studies on heuristic cues for credibility evaluation, user comments have been shown to serve as corrective, both positively and negatively affecting the perception of accompanying content. In an online survey with 417 German participants, we employed a 3 (author: female, male, and computer) × 2 (topic: stereotypically masculine and feminine) × 2 (comments: sexist and non-sexist) experimental design to investigate source and message credibility. Findings do not show differences in gender perception but between human authors (either female or male) and a computer (the control group). Covering counter-stereotypical topics indicates slightly less credibility for men and women if presented with non-sexist comments. In turn, sexist comments lead to slightly higher credibility, suggesting more elaborate engagement with sexism-affected content.

Pragmatic (In)appropriateness of Diminutives in Russian Language

The article discusses the issue of pragmatic meanings of diminutive forms in the Russian language from the point of view of politeness and speech act theories. The impact of diminutives on the illocutionary force and perlocutionary effects of speech acts raises the question of their appropriateness in various communicative situations. Since there is a negative bias towards diminutives among lay native speakers, it was decided to analyse their opinions, beliefs and feelings in order to define 1) the speakers illocutionary intentions; 2) contexts of use and 3) perlocutionary effects. The online articles, forums, posts on social networks and comments of Internet users were collected, with a total amount of 23 sources and 714 user comments. The emic and bottom-up approach referred to speakers intuition reveal 1) negative attitude towards diminutives in hierarchical relationships, 2) perception of diminutives as a positive politeness tool in low distance relationships, 3) perception of diminutives as a manipulative tool in unequal relationships. Thus, the appropriate use of diminutives calls for pragmatic skills to assess a communicative situation in terms of social variables, such as social distance and power, mutual costs and benefits, rights and obligations of interlocutors.

“Toxic atmosphere effect”: Uncivil online comments cue negative audience perceptions of news outlet credibility

Uncivil user comments have been found to have a negative effect on how people perceive an issue featured in the news, a news story, or a journalist who reports a news story. To advance this line of research, we draw on expectancy violations theory and the concept of heuristic cues to theorize the toxic atmosphere effect. We theorize that incivility in online comment threads could pose an even larger challenge to news organizations by cuing news audience members to perceive an entire news outlet—not just an individual story—as lacking in credibility. Based on two experiments in the United States (Study 1, n = 520; Study 2, n = 1056), we show that exposure to incivility can lead people to perceive a news outlet as less credible even though the incivility did not directly attack the news outlet. Such effects hold true even when people are exposed to comment threads in which the first several comments are civil. Democratic and business implications are discussed.