Discussion quality diffuses in the digital public square

Studies of online social influence have demonstrated that friends have important effects on many types of behavior in a wide variety of settings. However, we know much less about how influence works among relative strangers in digital public squares, despite important conversations happening in such spaces. We present the results of a study on large public Facebook pages where we randomly used two different methods--most recent and social feedback--to order comments on posts. We find that the social feedback condition results in higher quality viewed comments and response comments. After measuring the average quality of comments written by users before the study, we find that social feedback has a positive effect on response quality for both low and high quality commenters. We draw on a theoretical framework of social norms to explain this empirical result. In order to examine the influence mechanism further, we measure the similarity between comments viewed and written during the study, finding that similarity increases for the highest quality contributors under the social feedback condition. This suggests that, in addition to norms, some individuals may respond with increased relevance to high-quality comments.Comment: 10 pages, 6 figures, 2 table

Impacts of WeChat on Millennials’ Perceptions and Consumption Behaviors in the Hotel Industry

Social media, known as interactive Web 2.0 Internet-based applications, has deeply changed and reformed interpersonal communication and business operation with the wide spread of Internet and the development of technology. In the past few years, since mobile apps are becoming more and more popular, the access of social media is not limited to tablet computers only, but is also available for almost all kinds of smart phone devices, such as iPhone, Android, Symbian and so on. The function of social media is not confined to real- time message transmission or information sharing any more. It has expanded to a widely range of features, such as online purchase and payment, e-commerce business, and service for different types of social events. Social media plays an increasingly important role in daily personal life as well as in business activities. People are not merely considered as social media users, but also the component of social media itself. As a result, it is very crucial for people to realize the importance and impacts of social media, especially for those business operators. WeChat (Weixin in Chinses, literally “micro message”) is a cross-platform instant text and voice messaging communication service for multiple mobile devices, developed by Tecent in China, first released in the January of 2011. It is claimed to provide “the new way to connect” and create “a way of life”. It is free to download, install and register, and support all kinds of smart phone platforms with multiple language versions, such as Chinese, English, Japanese, French, and Spanish. WeChat provides its users different ways to communicate and interact with friends innovatively through instant text messaging, hold-to-talk voice messaging, group messaging, lively video sharing, location sharing, money transferring, and contact information sharing. Among all the WeChat users, Millennials is the majority. With the growing-up of Millennials, they are becoming more and more powerful and important to the society and will be the next target segmentation for most of the industries in the very near future. Especially for the hotel industry, the industry that urges to attract Millennials patrons for further substantial development, how to attract Millennials is becoming a critical issue for those hotel operators

Identifying Emotions in Social Media: Comparison of Word-emotion lexica

In recent years, emotions expressed in social media messages have become a vivid research topic due to their influence on the spread of misinformation and online radicalization over online social networks. Thus, it is important to correctly identify emotions in order to make inferences from social media messages. In this paper, we report on the performance of three publicly available word-emotion lexicons (NRC, DepecheMood, EmoSenticNet) over a set of Facebook and Twitter messages. To this end, we designed and implemented an algorithm that applies natural language processing (NLP) techniques along with a number of heuristics that reflect the way humans naturally assess emotions in written texts. In order to evaluate the appropriateness of the obtained emotion scores, we conducted a questionnaire-based survey with human raters. Our results show that there are noticeable differences between the performance of the lexicons as well as with respect to emotion scores the human raters provided in our surve

Antisocial Behavior in Online Discussion Communities

User contributions in the form of posts, comments, and votes are essential to the success of online communities. However, allowing user participation also invites undesirable behavior such as trolling. In this paper, we characterize antisocial behavior in three large online discussion communities by analyzing users who were banned from these communities. We find that such users tend to concentrate their efforts in a small number of threads, are more likely to post irrelevantly, and are more successful at garnering responses from other users. Studying the evolution of these users from the moment they join a community up to when they get banned, we find that not only do they write worse than other users over time, but they also become increasingly less tolerated by the community. Further, we discover that antisocial behavior is exacerbated when community feedback is overly harsh. Our analysis also reveals distinct groups of users with different levels of antisocial behavior that can change over time. We use these insights to identify antisocial users early on, a task of high practical importance to community maintainers.Comment: ICWSM 201

Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17