2,038 research outputs found
A taxonomy for deriving business insights from user-generated content
Deriving business insights from user-generated content (UGC) is a widely investigated phenomenon in information systems (IS) research. Due to its unstructured nature and technical constraints, UGC is still underutilized as a data source in research and practice. Using recent advancements in machine learning research, especially large language models (LLMs), IS researchers can possibly derive these insights more effectively. To guide and further understand the usage of these techniques, we develop a taxonomy that provides an overview of business insights derived from UGC. The taxonomy helps both practitioners and researchers identify, design, compare and evaluate the use of UGC in this IS context. Finally, we showcase an LLM-supported demo application that derives novel business insights and apply the taxonomy to it. In doing so, we show exemplary how LLMs can be used to develop new or extend existing NLP applications in the realm of IS
Study of Subjective and Objective Quality Assessment of Mobile Cloud Gaming Videos
We present the outcomes of a recent large-scale subjective study of Mobile
Cloud Gaming Video Quality Assessment (MCG-VQA) on a diverse set of gaming
videos. Rapid advancements in cloud services, faster video encoding
technologies, and increased access to high-speed, low-latency wireless internet
have all contributed to the exponential growth of the Mobile Cloud Gaming
industry. Consequently, the development of methods to assess the quality of
real-time video feeds to end-users of cloud gaming platforms has become
increasingly important. However, due to the lack of a large-scale public Mobile
Cloud Gaming Video dataset containing a diverse set of distorted videos with
corresponding subjective scores, there has been limited work on the development
of MCG-VQA models. Towards accelerating progress towards these goals, we
created a new dataset, named the LIVE-Meta Mobile Cloud Gaming (LIVE-Meta-MCG)
video quality database, composed of 600 landscape and portrait gaming videos,
on which we collected 14,400 subjective quality ratings from an in-lab
subjective study. Additionally, to demonstrate the usefulness of the new
resource, we benchmarked multiple state-of-the-art VQA algorithms on the
database. The new database will be made publicly available on our website:
\url{https://live.ece.utexas.edu/research/LIVE-Meta-Mobile-Cloud-Gaming/index.html}Comment: Accepted to IEEE Transactions on Image Processing, 2023. The database
will be publicly available by 1st week of July 202
Assessing Post Usage for Measuring the Quality of Forum Posts
It has become difficult to discover quality content within forums websites due to the increasing amount of UserGenerated Content (UGC) on the Web. Many existing websites have relied on their users to explicitly rate content quality. The main problem with this approach is that the majority of content often receives insufficient rating. Current automated content rating solutions have evaluated linguistic features of UGC but are less effective for different types of online communities. We propose a novel approach that assesses post usage to measure the quality of forum posts. Post usage can be viewed as implicit user ratings derived from their usage behaviour. The proposed model is validated against an operational forum using Matthews Correlation Coefficient to measure performance. Our model serves as a basis of exploring content usage to measure content quality in forums and other Web 2.0 platforms
DCVQE: A Hierarchical Transformer for Video Quality Assessment
The explosion of user-generated videos stimulates a great demand for
no-reference video quality assessment (NR-VQA). Inspired by our observation on
the actions of human annotation, we put forward a Divide and Conquer Video
Quality Estimator (DCVQE) for NR-VQA. Starting from extracting the frame-level
quality embeddings (QE), our proposal splits the whole sequence into a number
of clips and applies Transformers to learn the clip-level QE and update the
frame-level QE simultaneously; another Transformer is introduced to combine the
clip-level QE to generate the video-level QE. We call this hierarchical
combination of Transformers as a Divide and Conquer Transformer (DCTr) layer.
An accurate video quality feature extraction can be achieved by repeating the
process of this DCTr layer several times. Taking the order relationship among
the annotated data into account, we also propose a novel correlation loss term
for model training. Experiments on various datasets confirm the effectiveness
and robustness of our DCVQE model.Comment: Accepted by ACCV202
What drives the helpfulness of online reviews? A deep learning study of sentiment analysis, pictorial content and reviewer expertise for mature destinations.
User-generated content (UGC) is a growing driver of destination choice. Drawing on dual-process theories on how individuals process information, this study focuses on the role of central and peripheral information processing routes in the formation of consumers’ perceptions of the helpfulness of online reviews. We carried out a two-step process to address the perceived helpfulness of user-generated content, a sentiment analysis using advanced machine-learning techniques (deep learning), and a regression analysis. We used a database of 2,023 comments posted on TripAdvisor about two iconic Venetian cultural attractions, St. Mark’s Square (an open, free attraction) and the Doge’s Palace (a museum which charges an entry fee). Following the application of deep-learning techniques, we first identified which factors influenced whether a review received a “helpful” vote by means of logistic regression. Second, we selected those reviews which received at least one helpful vote to identify, through linear regression, the significant determinants of TripAdvisor users’ voting behaviour. The results showed that reviewer expertise is an influential factor in both free and paid-for attractions, although the impact of central cues (sentiment polarity, subjectivity and pictorial content) is different in both attractions. Our study suggests that managers should look beyond individual ratings and focus on the sentiment analysis of online reviews, which are shown to be based on the nature of the attraction (free vs. paid-for)
Supporting the use of user generated content in journalistic practice
Social media and user-generated content (UGC) are increasingly important features of journalistic work in a number of different ways. However, their use presents major challenges, not least because information posted on social media is not always reliable and therefore its veracity needs to be checked before it can be considered as fit for use in the reporting of news. We report on the results of a series of in-depth ethnographic studies of journalist work practices undertaken as part of the requirements gathering for a prototype of a social media verification ‘dashboard’ and its subsequent evaluation. We conclude with some reflections upon the broader implications of our findings for the design of tools to support journalistic work
Destination image online analyzed through user generated content: a systematic literature review
Destination Image is a concept that has been studied for a long time in tourism research. The question of how a destination is perceived by tourists and potential new guests is an important insight, especially for local tourism managers, in order to evaluate the implemented strategies and to plan further tactics.
Since the last two decades, due to a drastic digitalization, tourism research is now increasingly examining the Destination Image online. This creates new challenges in the selection of sources, methods, and in data collection.
The aim of the present study was to systematically capture the approach to analyze the online Destination Image through User Generated Content using studies from the last ten years. Therefore, a Systematic Literature Review on primary research from academic databases was conducted.
As a summary of the findings, a conceptual model was developed, based on the insights of the studies in the dataset, to contribute a guidance for the preparation phase of future online Destination Image research.
In short, the main findings are: TripAdvisor.com is the main source for online Destination Image analysis. Researchers recommend using the help of software and programming languages to collect and analyzed the data. Equally to earlier Destination Image studies, the main methods applied in online Destination Image analysis are quantitative content analysis, qualitative content analysis and sentiment analysis. In combination with the examination of cognitive and affective factors, co-occurrence analysis, and correlation analysis.
The present study has several limitations, which are: the loss of detail information due to reducing the studies to comparable key parameters, the absence of Anglo-American studies, due to the database selection as well as the lack of quality testing of the studies included.A Destination Image é um conceito que tem sido estudado há muito tempo na investigação turística. A questão de como o destino é visto pelos turistas e pelos potenciais novos hóspedes é uma perspectiva importante, especialmente para os gestores de turismo da região, a fim de avaliar as estratégias implementadas e de planear novas tácticas.
Desde as últimas duas décadas, ocorreu uma digitalização drástica, a investigação turística adaptou-se a este fenómeno e está agora a estudar cada vez mais a imagem do destino online. Esta alteração criou novos desafios na selecção de fontes, métodos, e na recolha de dados.
O objetivo do presente trabalho foi o de captar, de forma sistemática, as abordagens consideradas para analisar a imagem do destino online utilizando estudos dos últimos dez anos. Para este efeito, os estudos primários dos anos 2010-2020 das bases de dados académicos Web of Science, ProQuest e b-on, foram recolhidos utilizando palavras-chave de pesquisa pré-definidas.
O grupo de artigos obtidos como resultado foram subsequentemente sujeitos a avaliação de eligibilidade, como recomendado por Moher et al. (2009). Isto significa que os estudos que não cumpriam os critérios pré-definidos foram excluídos. Os critérios de inclusão foram: O trabalho académico tinha de ser uma referência primária de uma revista científica, escrita em inglês e a amostra analisada tinha de ter uma origem associada à comunicação nas social media online.
Posteriormente, os restantes 35 artigos foram transferidos para uma base de dados utilizando uma matriz de codificação. A matriz de codificação foi concebida para capturar os parâmetros-chave de cada estudo primário de uma forma padronizada e, portanto, comparável. Foi considerada informação geral, como o ano, localização e revista publicada, bem como informação temática específica, como o campo do turismo pesquisado e os meios analisados, juntamente com as categorias referentes à metodologia considerada, as ferramentas utilizadas e os resultados obtidos. A base de dados resultante foi então utilizada para obter declarações sobre a abordagem metodológica utilizada na análise da imagem de destinos online.
Como resumo dos resultados, foi desenvolvido um modelo conceptual, baseado nos conhecimentos obtidos a partir do grupo de artigos, que constituiu o conjunto de dados para análise, para contribuir com um guião para a fase de preparação de uma futura investigação sobre imagem dos destinos online.
Em resumo, as principais conclusões são: TripAdvisor.com é a principal fonte para a análise da imagem de destinos online. Os investigadores recomendam a utilização da ajuda de software e linguagens de programação para a recolha e análise dos dados. À semelhança de estudos anteriores de Destination Image, os principais métodos aplicados na análise imagem dos destinos online são a análise quantitativa do conteúdo, a análise qualitativa do conteúdo e a análise dos sentimentos. Em combinação com a análise dos fatores cognitivos e afectivos, análise de co-ocorrência, e análise de correlação.
O presente estudo tem várias limitações. Que são: a perda de informação detalhada devido à redução dos estudos a parâmetros-chave comparáveis, a ausência de estudos anglo-americanos, devido à selecção do banco de dados, bem como a falta de testes de qualidade dos estudos incluídos.(TurExperience - Tourist experiences' impacts on the destination image: searching for new opportunities to the Algarve”)
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