Large Amount Of User-generated Content Research Articles

A corpus-based real-time text classification and tagging approach for social data

With the rapid accumulation of large amounts of user-generated content through social media, social data reuse and integration have gained increasing attention recently. This has made it almost obsolete for software applications to collect, store, and work with their own data stored on local servers. While, with the provision of Application Programming Interfaces from the leading social networking sites, data acquisition and integration has become possible, the meaningful usage of such unstructured, non-uniform, and incoherent data collections needs special procedures of data summarization, understanding, and visualization. One particular aspect in this regard that needs special attention is the procedures for data (text snippets in the form of social media posts) categorization and concept tagging to filter out the relevant and most suitable data for the particular audience and for the particular purpose. In this regard, we propose a corpus-based approach for searching and successively categorizing and tagging the social data with relevant concepts in real time. The proposed approach is capable of addressing the semantical and morphological similarities, as well as domain-specific vocabularies of query strings and tagged concepts. We demonstrate the feasibility and application of our proposed approach in a web-based tool that allows searching Facebook posts and provides search results together with a concept map for further navigation, filtering, and refining of search results. The tool has been evaluated by performing multiple search queries, and resultant concept maps and annotated texts are analyzed in terms of their precision. The approach is thereby found effective in achieving its stated goal of classifying text snippets in real time.

Research on strategies of content production and operation in brand virtual communities from the perspective of user thinking—Taking Lululemon as an example

Background: As a platform for value co-creation, branded virtual communities retain a large amount of user-generated content, which not only serves as a convenient way for brands to grasp consumer preferences but also influences the purchasing intentions of users themselves and other users. Methods: This paper takes the Lululemon Weibo community and Xiaohongshu community as an example, firstly, the paper uses Python to capture the text content generated by the members of the two communities, and then we clean and pre-process the acquired content. After that, with the help of the LDA model and the confusion evaluation index, we thematize the content generated by the members of the two communities and identify the hot topics. Finally, the paper makes a comparative analysis of the hot topics in the two Lululemon virtual communities. Results: Members of the two virtual communities pay attention to same topics like “Wear Sharing”, “Life Sharing” and “Product Design”. But there are still differences in the focus and constitution of the two communities. Conclusions: Enterprises can identify the hot topics through their brand virtual communities, which provide the basis for the motivation of user-generated content within the community, as well as provide inspiration for enterprises to output content and marketing within the community; enterprises also need to ensure that members of the community continue to participate in community interactions and to ensure that the quality of user-generated content. High-quality user-generated content not only provides companies with effective information but also but also retains old members and attracts new members. Therefore, this paper also provides suggestions for enterprises from three perspectives: community content, community atmosphere, and platform environment.

Bi-directional emotional contagion: An analysis of chinese parents’ social media data

Analysis of social media during the COVID-19 quarantine period in Mainland China provides access to a large amount of user-generated content for sentiment analysis during this unexpected and stressful time period. This study focuses on emotions that were communicated in the context of interactions between parents and young children to explore their emotional attitudes and emotional contagion. Results suggest that positive emotional attitudes were more prevalent in parent-child interactions, which contrasts with previous research. In comparison to their children, parents expressed more negative moods. Nonetheless, Chinese preschoolers and their parents influenced each other's emotions with bi-directional effects, providing evidence of emotional contagion. Parents’ emotional transmission sometimes resulted in passive suppression by the young children. Emotions were manifested more through physical or behavioral interactions as opposed to verbal statements of feelings, especially during parent to child transmissions. The transmission of emotions from children to parents consisted mainly of two types: children's emotional catharsis and children's active emotional agency. The discussion explores explanations for the observed emotional contagion of positive emotions between parents and children, considers the role of power and agency during emotional contagion, and discusses the effects of Chinese socio-cultural factors on the sentiment analysis.

A social media analytic framework for improving operations and service management: A study of the retail pharmacy industry

The revolution in the digital economy is forcing the retail pharmacy industry to develop new business models to achieve operational excellence. A large amount of user-generated content on social media can be captured and analysed to help organisations gain insights into market requirements and enhance business intelligence. Accordingly, this study proposes an analytic framework for retail pharmacy organisations to: a) use social media and highlight the most-discussed topics by consumers, b) to identify the key areas for improvement based on the most negative comments received, and c) to determine the connections amongst the important concepts and enhance customer loyalty by adding values to consumers. We conduct an in-depth analysis on the Twitter platforms of the three largest retail pharmacy organisations in the UK: Boots, Lloyds and Superdrug. The findings show that issues with marketing, customer service and product are the key improvement areas for the retail pharmacies. Particularly, Boots received an overall better sentiment performance than Lloyds and Superdrug. We also determine the relationships amongst the important concepts discussed by consumers. The analysis generates insights into the use of social media for supporting pharmacy organisations in developing their social media strategies as well as improving their operations and service quality.

The Voice of Drug Consumers: Online Textual Review Analysis Using Structural Topic Model.

Many web-based pharmaceutical e-commerce platforms allow consumers to post open-ended textual reviews based on their purchase experiences. Understanding the true voice of consumers by analyzing such a large amount of user-generated content is of great significance to pharmaceutical manufacturers and e-commerce websites. The aim of this paper is to automatically extract hidden topics from web-based drug reviews using the structural topic model (STM) to examine consumers’ concerns when they buy drugs online. The STM is a probabilistic extension of Latent Dirichlet Allocation (LDA), which allows the consolidation of document-level covariates. This innovation allows us to capture consumer dissatisfaction along with their dynamics over time. We extract 12 topics, and five of them are negative topics representing consumer dissatisfaction, whose appearances in the negative reviews are substantially higher than those in the positive reviews. We also come to the conclusion that the prevalence of these five negative topics has not decreased over time. Furthermore, our results reveal that the prevalence of price-related topics has decreased significantly in positive reviews, which indicates that low-price strategies are becoming less attractive to customers. To the best of our knowledge, our work is the first study using STM to analyze the unstructured textual data of drug reviews, which enhances the understanding of the aspects of drug consumer concerns and contributes to the research of pharmaceutical e-commerce literature.

A Participatory Journalism Management Platform: Design, Implementation and Evaluation

During the last two decades, citizens’ participation in news production process has attracted significant interest from both academia and the media industry. Media production and consumption have been altered considerably and traditional concepts, such as gatekeeping, have been under discussion. Many news organisations include in their websites tools and applications that allow users to be active consumers or even co-producers of journalistic content, by liking, sharing, commenting and submitting material. At the same time, large amounts of user-generated content are uploaded every day on social media platforms. Subsequently, media organisations must deal with continually available information which requires management, classification and evaluation not only to keep high journalistic standards, but also to avoid problems. The latter category can include grammar mistakes, fake or misleading information and hate speech. All the above-mentioned parameters highlight the obvious need for platforms that can support journalism manage practice. Such a platform should utilise semantic technologies, which can support organised collection and moderation of content in an effective way and in short time. This study discusses the design and the implementation of a participatory journalism management platform.

Applying big data analytics to support Kansei engineering for hotel service development

PurposeLeisure and tourism activities have proliferated and become important parts of modern life, and the hotel industry plays a necessary role in the supply for and demand from consumers. The purpose of this paper is to develop guidelines for hotel service development by applying a service development approach integrating Kansei engineering and text mining.Design/methodology/approachThe online reviews represent the voice of customers regarding the products and services. Consumers’ online comments might become a key factor for consumers choosing hotels when planning their tourism itinerary. With the framework of Kansei engineering, this paper adopts text mining to extract the sets of Kansei words and hotel service characteristics from the online contents as well as the relationships among Kansei words, service characteristics and these two sets. The relationships are generated by using link analysis, and then the guidelines for hotel service development are proposed based on the obtained relationships.FindingsThe results of the present research can provide the hotel industry a comprehensive understanding of hotels’ customers opinions, and can offer specific advice on how to differentiate one’s products and services from competitors’ in order to improve customer satisfaction and increase hotels’ performance in the end. Finally, this study finds out the service development guidelines to meet customers’ requirements which can provide suggestions for hotel managers. The implications both for academic and industry are also drawn based on the obtained results.Originality/valueNow, in the internet era, consumers can comment on their hotel living experience directly through the internet. The large amount of user-generated content (UGC) provided by consumers also provides chances for the hospitality industry to understand consumers’ opinions through online review mining. The UGC with consumers’ opinions to hotel services can be continuously collected and analyzed by hoteliers. Therefore, this paper demonstrates how to apply the hybrid approach integrating Kansei engineering and online review mining to hotel service development.

Blurring Political Leanings of Messages on Social Networks Using Deniable Steganography

While social network services have become popular platforms where large amounts of user-generated content are published, individuals, election campaigns, and political organizations leverage the services to publish, promote, and exchange political messages. However, sometimes, political discussions resulted in heated debates, doxing, and even cyberbullying. These conflicts may decrease the engagement of users and prevent them from publishing articles with apparent political orientation. To help users to post political opinion among the Internet freely, in this paper, we employ the concept of deniable encryption to generate neutral-like articles composed of two critics toward both sides of the discussion, while one of the critics is the actual message from the author to particular recipients. For receivers who know the credential information (e.g., key and codebook), they can communicate with each other by extracting the real information from the neutral-like articles. For people with the opposite stand, they cannot claim the message that is supporting specific political entity, as it contains disapproval to both stands; besides, they cannot prove that which part of the message is the plaintext even if they intercept the messages. We provide a generalized process of our proposal and present a practical scenario using transcripts, Facebook posts of Donald Trump, and posts of Hillary Clinton to demonstrate the usability of our scheme.

Social information discovery enhanced by sentiment analysis techniques

In recent years, the massive diffusion of social networks has made available a large amount of user-generated content, for the most part in the form of textual data that contain people’s thoughts and emotions about a great variety of topics. In order to exploit these publicly available information, in this work we introduce a social information discovery system which elaborates simultaneously over more-than-one social network in an integrated scenario. The system is designed to ensure flexibility and scalability, thus enabling for (near-)real-time analysis even in case of high rates of content’s creation and large amounts of heterogeneous data. Furthermore, a noise detection technique ensures a high relevance of analyzed posts/tweets to the domain of interest. We also propose a lexicon-based sentiment analysis algorithm to extract and measure users’ opinion, in order to support collaboration and open innovation. Polysemous words and negations are typically challenging for lexicon-based approaches: for this reason, we introduce both a word sense disambiguation algorithm and a negation handling technique. Experiments on several datasets have proven that the combined use of both techniques improves the classification accuracy on 3-class sentiment analysis.

Characterisation of mental health conditions in social media using Informed Deep Learning

The number of people affected by mental illness is on the increase and with it the burden on health and social care use, as well as the loss of both productivity and quality-adjusted life-years. Natural language processing of electronic health records is increasingly used to study mental health conditions and risk behaviours on a large scale. However, narrative notes written by clinicians do not capture first-hand the patients’ own experiences, and only record cross-sectional, professional impressions at the point of care. Social media platforms have become a source of ‘in the moment’ daily exchange, with topics including well-being and mental health. In this study, we analysed posts from the social media platform Reddit and developed classifiers to recognise and classify posts related to mental illness according to 11 disorder themes. Using a neural network and deep learning approach, we could automatically recognise mental illness-related posts in our balenced dataset with an accuracy of 91.08% and select the correct theme with a weighted average accuracy of 71.37%. We believe that these results are a first step in developing methods to characterise large amounts of user-generated content that could support content curation and targeted interventions.

Mining Coherent Topics With Pre-Learned Interest Knowledge in Twitter

Discovering semantic coherent topics from the large amount of user-generated content (UGC) in social media would facilitate many downstream applications of intelligent computing. Topic models, as one of the most powerful algorithms, have been widely used to discover the latent semantic patterns in text collections. However, one key weakness of topic models is that they need documents with certain length to provide reliable statistics for generating coherent topics. In Twitter, the users’ tweets are mostly short and noisy. Observations of word co-occurrences are incomprehensible for topic models. To deal with this problem, previous work tried to incorporate prior knowledge to obtain better results. However, this strategy is not practical for the fast evolving UGC in Twitter. In this paper, we first cluster the users according to the retweet network, and the users’ interests are mined as the prior knowledge. Such data are then applied to improve the performance of topic learning. The potential cause for the effectiveness of this approach is that users in the same community usually share similar interests, which will result in less noisy sub-data sets. Our algorithm pre-learns two types of interest knowledge from the data set: the interest-word-sets and a tweet-interest preference matrix . Furthermore, a dedicated background model is introduced to judge whether a word is drawn from the background noise. Experiments on two real life twitter data sets show that our model achieves significant improvements over state-of-the-art baselines.

The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews.

BackgroundMany Web-based health care platforms allow patients to evaluate physicians by posting open-end textual reviews based on their experiences. These reviews are helpful resources for other patients to choose high-quality doctors, especially in countries like China where no doctor referral systems exist. Analyzing such a large amount of user-generated content to understand the voice of health consumers has attracted much attention from health care providers and health care researchers.ObjectiveThe aim of this paper is to automatically extract hidden topics from Web-based physician reviews using text-mining techniques to examine what Chinese patients have said about their doctors and whether these topics differ across various specialties. This knowledge will help health care consumers, providers, and researchers better understand this information.MethodsWe conducted two-fold analyses on the data collected from the “Good Doctor Online” platform, the largest online health community in China. First, we explored all reviews from 2006-2014 using descriptive statistics. Second, we applied the well-known topic extraction algorithm Latent Dirichlet Allocation to more than 500,000 textual reviews from over 75,000 Chinese doctors across four major specialty areas to understand what Chinese health consumers said online about their doctor visits.ResultsOn the “Good Doctor Online” platform, 112,873 out of 314,624 doctors had been reviewed at least once by April 11, 2014. Among the 772,979 textual reviews, we chose to focus on four major specialty areas that received the most reviews: Internal Medicine, Surgery, Obstetrics/Gynecology and Pediatrics, and Chinese Traditional Medicine. Among the doctors who received reviews from those four medical specialties, two-thirds of them received more than two reviews and in a few extreme cases, some doctors received more than 500 reviews. Across the four major areas, the most popular topics reviewers found were the experience of finding doctors, doctors’ technical skills and bedside manner, general appreciation from patients, and description of various symptoms.ConclusionsTo the best of our knowledge, our work is the first study using an automated text-mining approach to analyze a large amount of unstructured textual data of Web-based physician reviews in China. Based on our analysis, we found that Chinese reviewers mainly concentrate on a few popular topics. This is consistent with the goal of Chinese online health platforms and demonstrates the health care focus in China’s health care system. Our text-mining approach reveals a new research area on how to use big data to help health care providers, health care administrators, and policy makers hear patient voices, target patient concerns, and improve the quality of care in this age of patient-centered care. Also, on the health care consumer side, our text mining technique helps patients make more informed decisions about which specialists to see without reading thousands of reviews, which is simply not feasible. In addition, our comparison analysis of Web-based physician reviews in China and the United States also indicates some cultural differences.

Crowdsourcing a Collective Sense of Place.

Place can be generally defined as a location that has been assigned meaning through human experience, and as such it is of multidisciplinary scientific interest. Up to this point place has been studied primarily within the context of social sciences as a theoretical construct. The availability of large amounts of user-generated content, e.g. in the form of social media feeds or Wikipedia contributions, allows us for the first time to computationally analyze and quantify the shared meaning of place. By aggregating references to human activities within urban spaces we can observe the emergence of unique themes that characterize different locations, thus identifying places through their discernible sociocultural signatures. In this paper we present results from a novel quantitative approach to derive such sociocultural signatures from Twitter contributions and also from corresponding Wikipedia entries. By contrasting the two we show how particular thematic characteristics of places (referred to herein as platial themes) are emerging from such crowd-contributed content, allowing us to observe the meaning that the general public, either individually or collectively, is assigning to specific locations. Our approach leverages probabilistic topic modelling, semantic association, and spatial clustering to find locations are conveying a collective sense of place. Deriving and quantifying such meaning allows us to observe how people transform a location to a place and shape its characteristics.

Actionable Social Media Competitive Analytics For Understanding Customer Experiences

ABSTRACTA large amount of user-generated content is now freely available on social media sites. To increase their competitive advantage, companies need to monitor and analyze not only the customer-generated content on their own social media sites, but also the content on their competitors’ social media sites. In this article, we describe a framework to integrate several techniques including quantitative analysis, text mining, and sentiment analysis for analyzing and comparing social media content from business competitors. Specifically, we conducted an in-depth case study which applies our developed framework to the analysis and comparison of social media content on the Facebook sites of the three largest drugstore chains in the United States: Walgreens, CVS, and Rite Aid. We found similarities and differences in the social media use among the three drugstore chains. We discuss the implications of our findings and provide recommendations to help companies develop their social media competitive analysis strategies.

Timeprints for identifying social media users with multiple aliases

Many people who discuss sensitive or private issues on social media services are using pseudonyms or aliases in order to not reveal their true identity, while using their usual, non-private accounts when posting messages on less sensitive issues. Previous research has shown that if those individuals post large amounts of user-generated content, stylometric techniques can be used to identify the author based on the characteristics of the textual content. In this article we show how an author’s identity can be unmasked in a similar way using various time features (e.g., period of the day and the day of the week when a user’s posts have been published). We combine several different time features into a timeprint, which can be seen as a type of fingerprint when identifying users on social media. We use supervised machine learning (i.e., author identification) and unsupervised alias matching (similarity detection) in a number of different experiments with forum data to get an understanding of to what extent timeprints can be used for identifying users in social media, both in isolation and when combined with stylometric features. The obtained results show that timeprints indeed can be a very powerful tool for both author identification and alias matching in social media.

Multi-lingual opinion mining on YouTube

In order to successfully apply opinion mining (OM) to the large amounts of user-generated content produced every day, we need robust models that can handle the noisy input well yet can easily be adapted to a new domain or language. We here focus on opinion mining for YouTube by (i) modeling classifiers that predict the type of a comment and its polarity, while distinguishing whether the polarity is directed towards the product or video; (ii) proposing a robust shallow syntactic structure (STRUCT) that adapts well when tested across domains; and (iii) evaluating the effectiveness on the proposed structure on two languages, English and Italian. We rely on tree kernels to automatically extract and learn features with better generalization power than traditionally used bag-of-word models. Our extensive empirical evaluation shows that (i) STRUCT outperforms the bag-of-words model both within the same domain (up to 2.6% and 3% of absolute improvement for Italian and English, respectively); (ii) it is particularly useful when tested across domains (up to more than 4% absolute improvement for both languages), especially when little training data is available (up to 10% absolute improvement) and (iii) the proposed structure is also effective in a lower-resource language scenario, where only less accurate linguistic processing tools are available.

Social media competitive analysis and text mining: A case study in the pizza industry

Social media have been adopted by many businesses. More and more companies are using social media tools such as Facebook and Twitter to provide various services and interact with customers. As a result, a large amount of user-generated content is freely available on social media sites. To increase competitive advantage and effectively assess the competitive environment of businesses, companies need to monitor and analyze not only the customer-generated content on their own social media sites, but also the textual information on their competitors’ social media sites. In an effort to help companies understand how to perform a social media competitive analysis and transform social media data into knowledge for decision makers and e-marketers, this paper describes an in-depth case study which applies text mining to analyze unstructured text content on Facebook and Twitter sites of the three largest pizza chains: Pizza Hut, Domino's Pizza and Papa John's Pizza. The results reveal the value of social media competitive analysis and the power of text mining as an effective technique to extract business value from the vast amount of available social media data. Recommendations are also provided to help companies develop their social media competitive analysis strategy.

RCCtrust: A Combined Trust Model for Electronic Community

Previous trust models are mainly focused on reputational mechanism based on explicit trust ratings. However, the large amount of user-generated content and community context published on Web is often ignored. Without enough information, there are several problems with previous trust models: first, they cannot determine in which field one user trusts in another, so many models assume that trust exists in all fields. Second some models are not able to delineate the variation of trust scales, therefore they regard each user trusts all his friends to the same extent. Third, since these models only focus on explicit trust ratings, so the trust matrix is very sparse. To solve these problems, we present RCC trust-a trust model which combines Reputation-, Content- and Context-based mechanisms to provide more accurate, fine-grained and efficient trust management for the electronic community. We extract trust-related information from user-generated content and commmunity context from Web to extend reputation-based trust models. We introduce role-based and behavior-based reasoning functionalities to infer users' interests and category-specific trust relationships. Following the study in sociology, RCCtrust exploits similarities between pairs of users to depict differentiated trust scales. The experimental results show that RCCtrust outperforms pure user similarity method and linear decay trust-aware technique in both accuracy and coverage for a Recommender System.