Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
73,533
result(s) for
"User generated content"
Sort by:
Is Neutral Really Neutral? The Effects of Neutral User-Generated Content on Product Sales
This article aims to specify the performance implications of neutral user-generated content (UGC) on product sales by differentiating mixed-neutral UGC, which contains an equal amount of positive and negative claims, from indifferent-neutral UGC, which includes neither positive nor negative claims. The authors propose that positive and negative UGC only provide opportunities for consumers to process product-related information, whereas both mixed- and indifferent-neutral UGC affect consumers’ motivation and ability to process positive and negative UGC. The results of three studies using multiple measures (text and numerical UGC), contexts (automobiles, movies, and tablets), and methods (empirical and behavioral experiment) indicate contrasting premium and discount effects such that mixed-neutral UGC amplifies the effects of positive and negative UGC, whereas indifferent-neutral UGC attenuates them. Empirical evidence further indicates that ignoring mixed- or indifferent-neutral UGC leads to substantial under- or overestimates of the effects of positive and negative UGC. The effects of neutral UGC on product sales thus are not truly neutral, and the direction of the bias depends on both the type of UGC and the distribution of positive and negative UGC.
Journal Article
Understanding the world of user-generated content
Describes how to use those parts of the Internet that are interactive, sometimes known as \"Web 2.0,\" including Wikipedia, blogs, podcasts, video sharing, and other types of user-generated content.
Book
Behind the Screen
An eye-opening look at the invisible workers who protect us from seeing humanity's worst on today's commercial internetSocial media on the internet can be a nightmarish place. A primary shield against hateful language, violent videos, and online cruelty uploaded by users is not an algorithm. It is people. Mostly invisible by design, more than 100,000 commercial content moderators evaluate posts on mainstream social media platforms: enforcing internal policies, training artificial intelligence systems, and actively screening and removing offensive material-sometimes thousands of items per day. Sarah T. Roberts, an award-winning social media scholar, offers the first extensive ethnographic study of the commercial content moderation industry. Based on interviews with workers from Silicon Valley to the Philippines, at boutique firms and at major social media companies, she contextualizes this hidden industry and examines the emotional toll it takes on its workers. This revealing investigation of the people \"behind the screen\" offers insights into not only the reality of our commercial internet but the future of globalized labor in the digital age.
eBook
Scandal in a digital age
This book explores the way today's interconnected and digitized world--marked by social media, over-sharing, and blurred lines between public and private spheres--shapes the nature and fallout of scandal in a frenzied media environment. Today's digitized world has erased the former distinction between the public and private self in the social sphere. Scandal in a Digital Age marries scholarly research on scandal with journalistic critique to explore how our Internet culture driven by (over)sharing and viral, visual content impacts the occurrence of scandal and its rapid spread online through retweets and reposts. No longer are examples of scandalous behavior merely reported in the news. Today, news consumers can see the visual evidence of salacious behavior whether through an illicit tweet or video with a simple click. And we can't help but click. -- Back cover.
Book
Impact of YouTube User‐Generated Content on News Dissemination and Youth Information Reception
Background User‐generated content (UGC) on YouTube has reshaped news dissemination, fostered engagement, raised concerns about credibility, algorithmic influence and the spread of misinformation. This study addresses the gap in understanding how UGC engagement, trust and algorithmic awareness influence digital news consumption. Methods A convergent parallel mixed‐methods design was employed, integrating survey data (n = 100), qualitative interviews and content analysis of 200 YouTube news videos. Data were collected over 6 weeks. Quantitative analyses included ANOVA, multivariate regression and structural equation modelling (SEM), while qualitative data were thematically analysed to contextualise statistical findings. Results UGC news consumption (M = 3.21, SD = 1.14) exceeded traditional news (M = 2.95, SD = 1.20), with trust in UGC (M = 3.48, SD = 1.05) surpassing traditional sources (M = 3.12, SD = 1.17). SEM analysis confirmed that UGC engagement significantly increased trust (β = 0.42, p < 0.001), while algorithmic influence negatively affected trust (β = −0.33, p = 0.015). Sensationalist content attracted higher engagement (30.0%) but had lower credibility, with misinformation prevalent in 38.0% of analysed videos. Conclusion Findings highlight the need for platform transparency, stronger content verification and policy interventions to balance engagement‐driven algorithms and news credibility. Media literacy initiatives are crucial for equipping users with the critical evaluation skills they need.
Journal Article
Wikipedia : the company and its founders
This volume examines the history of Wikipedia, and the lives and careers of its founders.
Book
Harnessing the social web to enhance insights into people’s opinions in business, government and public administration
Transparency, participation, and collaboration are the core pillars of open government. For the systematic integration of citizens and other stakeholders into the policy and public value creation process, their opinions, wishes, and complaints first need to be received. In the future, including user-generated content from social media will become a main channel for the enrichment of this information base for public administrative bodies and commercial firms. However, the sheer speed of growth of this constantly updated data pool makes manual work infeasible. The automated gathering, combination, analysis, and visualization of user-generated content from various sources and multiple languages is therefore imperative.
In this study, we present a design science research approach to develop a general framework (‘
MarketMiner
’) to handle large amounts of foreign-language user-generated content. As a first empirical application, we implement the framework in the automotive industry by analyzing Chinese automotive forums for the benefit of English-speaking users. At the same time, the ideas, methods, and insights are transferred to the public sector context, especially in light of the current challenges of a high number of political refugees from Arabic countries entering into the European Union.
The results are promising in that
MarketMiner
can dramatically improve the utilization of multi-language, multi-source social media content. The modular set-up of the artifact allows an easy transfer to additional areas of application.
Journal Article
The Power of Brand Selfies
Smartphones have made it nearly effortless to share images of branded experiences. This research classifies social media brand imagery and studies user response. Aside from packshots (standalone product images), two types of brand-related selfie images appear online: consumer selfies (featuring brands and consumers' faces) and an emerging phenomenon the authors term \"brand selfies\" (invisible consumers holding a branded product). The authors use convolutional neural networks to identify these arche-types and train language models to infer social media response to more than a quarter-million brand-image posts (185 brands on Twitter and Instagram). They find that consumer-selfie images receive more sender engagement (i.e., likes and comments), whereas brand selfies result in more brand engagement, expressed by purchase intentions. These results cast doubt on whether conventional social media metrics are appropriate indicators of brand engagement. Results for display ads are consistent with this observation, with higher click-through rates for brand selfies than for consumer selfies. A controlled lab experiment suggests that self-reference is driving the differential response to selfie images. Collectively, these results demonstrate how (interpretable) machine learning helps extract marketing-relevant information from unstructured multimedia content and that selfie images are a matter of perspective in terms of actual brand engagement.
Journal Article