A Knowledge Adoption Model Based Framework for Finding Helpful User-Generated Contents in Online Communities

Many online communities allow their members to provide information helpfulness judgments that can be used to guide other users to useful contents quickly. However, it is a serious challenge to solicit enough user participation in providing feedbacks in online communities. Existing studies on assessing the helpfulness of user-generated contents are mainly based on heuristics and lack of a unifying theoretical framework. In this article we propose a text classification framework for finding helpful user-generated contents in online knowledge-sharing communities. The objective of our framework is to help a knowledge seeker find helpful information that can be potentially adopted. The framework is built on the Knowledge Adoption Model that considers both content-based argument quality and information source credibility. We identify 6 argument quality dimensions and 3 source credibility dimensions based on information quality and psychological theories. Using data extracted from a popular online community, our empirical evaluations show that all the dimensions improve the performance over a traditional text classification technique that considers word-based lexical features only

Towards the Development of a DSS Supporting the Integration of Crowdsourcing in Theory Testing: Conceptual Framework and Model

Through innovations in technologies, electronic commerce (EC) has evolved into a new stream of commerce called social commerce (SC). This paper defines SC as a combination of performing EC activities and socializing on online communities via facilitating technologies to make purchase decisions. SC adoption has recently gained attention among interested scholars; yet, since SC is a new technology, studies are limited. The current research gap is twofold: First, no studies have yet examined the impact of government involvement on SC adoption. Second, the impact of social factors (e.g. social support and electronic word of mouth) on SC adoption in the Saudi Arabian context has not yet been investigated. This in-progress research aims to provide guidance to interested stakeholders on how to encourage consumers to adopt SC in Saudi Arabia. The study uses the theory of planned behaviour, along with other external factors, as a theoretical model

eWOM & Referrals in Social Network Services

If a few decades ago the development of the Internet was instrumental in the interconnection between markets, nowadays the services provided by Web 2.0, such as social network sites (SNS) are the cutting edge. A proof of this trend is the exponential growth of social network users. The main objective of this work is to explore the mechanisms that promote the transmission and reception (WOM and referrals) of online opinions, in the context of the SNS, by buyers of travel services. The research includes some research lines: technology acceptance model (TAM), Social Identification Theory and Word-of-Mouth communication in virtual environment (eWOM). Based on these theories an explicative model has been proposed applying SEM analysis to a sample of SNS users’ of tourist service buyers. The results support the majority of the hypotheses and some relevant practical and theoretical implications have been pointed out for tourist managers

#Bieber + #Blast = #BieberBlast: Early Prediction of Popular Hashtag Compounds

Compounding of natural language units is a very common phenomena. In this paper, we show, for the first time, that Twitter hashtags which, could be considered as correlates of such linguistic units, undergo compounding. We identify reasons for this compounding and propose a prediction model that can identify with 77.07% accuracy if a pair of hashtags compounding in the near future (i.e., 2 months after compounding) shall become popular. At longer times T = 6, 10 months the accuracies are 77.52% and 79.13% respectively. This technique has strong implications to trending hashtag recommendation since newly formed hashtag compounds can be recommended early, even before the compounding has taken place. Further, humans can predict compounds with an overall accuracy of only 48.7% (treated as baseline). Notably, while humans can discriminate the relatively easier cases, the automatic framework is successful in classifying the relatively harder cases.Comment: 14 pages, 4 figures, 9 tables, published in CSCW (Computer-Supported Cooperative Work and Social Computing) 2016. in Proceedings of 19th ACM conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2016

Electronic word of mouth in social media: The common characteristics of retweeted and favourited marketer-generated content posted on Twitter

Marketers desire to utilise electronic word of mouth (eWOM) marketing on social media sites. However, not all online content generated by marketers has the same effect on consumers; some of them are effective while others are not. This paper aims to examine different characteristics of marketer-generated content (MGC) that of which one lead users to eWOM. Twitter was chosen as one of the leading social media sites and a content analysis approach was employed to identify the common characteristics of retweeted and favourited tweets. 2,780 tweets from six companies (Booking, Hostelworld, Hotels, Lastminute, Laterooms and Priceline) operating in the tourism sector are analysed. Results indicate that the posts which contain pictures, hyperlinks, product or service information, direct answers to customers and brand centrality are more likely to be retweeted and favourited by users. The findings present the main eWOM drivers for MGC in social media.Abdulaziz Elwalda and Mohammed Alsagga

Making "fetch" happen: The influence of social and linguistic context on nonstandard word growth and decline

In an online community, new words come and go: today's "haha" may be replaced by tomorrow's "lol." Changes in online writing are usually studied as a social process, with innovations diffusing through a network of individuals in a speech community. But unlike other types of innovation, language change is shaped and constrained by the system in which it takes part. To investigate the links between social and structural factors in language change, we undertake a large-scale analysis of nonstandard word growth in the online community Reddit. We find that dissemination across many linguistic contexts is a sign of growth: words that appear in more linguistic contexts grow faster and survive longer. We also find that social dissemination likely plays a less important role in explaining word growth and decline than previously hypothesized

Efecto de la confianza en la lealtad y el eWOM en las comunidades virtuales de marca.

La confianza en la marca y en la Comunidad Virtual de Marca (CVM) pueden contribuir a la generación de lealtad a la marca y eWOM positivo. Sin embargo, no han sido muchos los estudios empíricos que han incluido ambos tipos de confianza en la evaluación de los resultados de las CVM. Por lo tanto, este trabajo tiene como objetivo explorar cómo la confianza en la marca y la confianza en la comunidad influyen en la lealtad y en el eWOM. Para ello se emplearon datos procedentes de una encuesta realizada a usuarios de CVM que fueron analizados mediante la técnica PLS. Los resultados confirman que la confianza en la marca influye en la lealtad y en el eWOM tanto directamente, como indirectamente a través de la confianza en la CVM. Además, la lealtad favorece la generación de eWOM. Las implicaciones para la práctica de marketing son comentadas.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Social media and tourism : a wishful relationship

For decades hospitality firms were used to domain the communication process. Thematic social network sites such as TripAdvisor became very important tools for travelers when deciding which hotels to book, and what restaurants and tourist attractions to visit, been a visible part of tourism communication evolution. Evidence suggests that e-WOM serves as a primary information source when tourists choose destinations, hotels, and other experiences. The role and use of social media in tourists’ decision making has been widely discuss in tourism and hospitality research, especially in the research phase of the tourist’ travel planning process. With the wide adoption of social media the influence of customers’ word-of-mouth increased and influences not only the research phase, but the repetition and overall customers’ experiences. To answer these questions a model assessing e-wom was developed and data was gathering from TripAdvisor regarding customer’s opinion in restaurant experiences. The results found establish the bases for understanding tourists’ engagement level and profiles.N/