Kind Of User Generated Content Research Articles

Do different kinds of user-generated content in online brand communities really work?

PurposeDue to the fact that user-generated content (UGC) and online brand communities (OBCs) are gaining popularity, the purpose of this paper is to identify the type of UGC that has a real effect on product success, in terms of the number of owners, within a popular OBC associated with video games.Design/methodology/approachDifferent types of UGC for 205 video games were manually collected (the number of positive and negative comments, discussions, screenshots, artwork, videos, guides developed by users and the presence of a workshop) to test their influence on product success. The proposed hypotheses were tested using multiple ridge regression analysis.FindingsResults show that users look for simple and quick reviews and content about products in OBCs (i.e. guides developed by users, comments, artwork and screenshots). However, results also show that users do not guide their purchases based on UGC when the process of gaining understanding is more time consuming (i.e. reading discussions, watching videos) or requires more active involvement (i.e. workshop presence).Originality/valueLimited research has been conducted on the type of UGC found in OBCs. This study contributes to the understanding of the potential influence of different types of UGC on product success. In addition, it offers managerial insights for companies into how to manage content in online communities.

Collaborative recommendation with user generated content

In the age of Web 2.0, user generated content (UGC), such as user review and social tag, ubiquitously exists on the Internet. Although there exist different kinds of UGC in recommender systems, the existing works only studied a single kind of UGC in each of their papers. Thus, the previous works lose a chance to uncover the similar effects of different kinds of UGC in recommender systems. In this paper, we propose a unified way to utilize various types of UGC to enhance the recommendation accuracy. We build two novel statistical models, which are based on collaborative filtering and topic modeling. Incorporating UGC text, one model focuses on learning user preferences, and the other model aims to learn user preferences and item aspects jointly. With an effective parameter estimation algorithm, our models can not only acquire prediction values of missing ratings, but also produce interpretable topics. We conducted comprehensive experiments on three real-world datasets. The experimental results demonstrate that our proposed models can achieve large improvements compared to several well-known baseline models.

The Impacts of Electronic Word of Mouth on Brand Equity in the Context of Social Media

Social media has become the driving force which transforms the web into an interactive information andcommunications technology device. Social media has a significant role in influencing customer’s choice inselecting products and services based on the customers’ feedbacks that appeared in the weblogs, web sites,online boards and other kinds of user-generated content (Raman, 2009). It is indeed important to remember thatbrand equity is no longer valued by large sums of money that companies invest; instead customers are dictatingthe value of brand equity by what they are saying to each other. Therefore, this study will focus on evaluating theroles of various brand equity constructs (including brand loyalty, brand association, brand awareness and brandimage) in mediating the interrelation among electronic word of mouth and the dimensions of brand equity in thecontext of social media. There were total of 300 usable questionnaires were collected in this research. Thefinding revealed that there is an indirect inter-relationship between electronic word of mouth and the dimensionof brand equity, mediated by the respective various brand equity constructs.

Copyright-Exempt Nonprofits: A Simple Proposal to Spur Innovation

New types of social networks have recently emerged that have facilitated the growth of a different kind of user-generated content: curation. The user finds various content from the Internet and then organizes or “curates” the content in a social network platform in a way that better serves the user’s purpose. For example, on Pinterest, users can “pin” content from the web onto their virtual “pinboards” that have topical categories of the users’ choice. The content typically involves a photograph (in reduced size) from the original web page that is then displayed on the user’s pinboard. Clicking on the photograph displays the actual photograph (or a copy of it), but displayed on the Pinterest website, with attribution and a link to the original website. In this way, the user is able to organize content from the web visually by the user’s own categories, such as favorite recipes, yoga instruction, furniture to buy, or places to travel.In the background of Internet curation lurks copyright law, waiting to rear its proverbial head. The kind of activity Pinterest facilitates may constitute copyright infringement, although at least an arguable defense based on fair use or the DMCA safe harbors exists. If a lawsuit is filed, an adverse decision against the site could put in jeopardy this emerging practice of content curation — quashing it generally.This Article offers a way for Congress to address, more generally, the recurring problem created by new speech technologies that disrupt copyright law. The proposal is for Congress to enact a copyright exemption for qualifying 501(c)(3) “charitable” non-profit entities. These entities will be “copyright exempt” for a limited purpose to develop and deploy new kinds of Internet speech technologies free for the public’s use. Under the proposed Nonprofit Internet Copyright Exemption (NICE) Act, such nonprofits and their users will enjoy a copyright exemption for noncommercial activity involving user-generated content, including Internet curation.

Textual and Informational Characteristics of Drug-Related Content on Three Kinds of Websites

This paper develops techniques to mine medical and health-related social media content for information and opinions about diseases and treatments. This would allow users to benefit from the numerous postings of people’s experiences scattered all over the Web. This paper reports the results of a textual analysis of user-generated content on drug-related online sites. The aim is to determine what kinds of content can be expected on these sites, from linguistic and information points-of-view. User postings were harvested from two websites carrying different kinds of user-generated content and compared to information on the same drugs from a hospital information portal. The corpus was analyzed to identify what kinds of drugs were often reviewed, the vocabulary and medical concepts used, and the textual characteristics such as length of postings, sentence length, and part-of-speech distribution. Although drug-related user-generated content is very different from editorial content, the results of this project show that it provides useful information on many drugs. From a linguistic point-of-view, user-generated content is simpler in language and more informal in style than editorial content, but still contains useful health information that tends to be patient-oriented.