User-generated Information Research Articles

Analyzing Public Perceptions and User Sentiments on Tweets: A Machine Learning Approach

The blast of social media has driven to a tremendous sum of user-generated information, especially on stages like Twitter where individuals express their sees and feelings openly. Opinion mining develops as a capable instrument to extricate profitable bits of knowledge from this unstructured information. Twitter serves as a rich resource for comprehending user sentiments and public viewpoints across diverse domains such as politics, current events, consumer behaviors, and brand perception. With the exponential growth in tweet volume, manual analysis of this vast dataset proves impractical. This paper digs into opinion investigation strategies particularly custom fitted for Twitter. Therefore, we are going to analyze different opinions or emotions of users and for this we will use a sentiment analysis approach. Sentiment analysis is an approach to analyzing data and capturing the emotions it embodies. Twitter Sentiment Analysis is the application of sentiment analysis to data from Twitter (Tweets) to extract the sentiments sent by users. The paper moreover examines common assessment measurements utilized to survey the adequacy of these procedures. By giving a comparative investigation, this overview points to prepare analysts and specialists with a comprehensive understanding of estimation investigation on Tweets. This information can be saddled for different applications, such as gaging open conclusion on current occasions, observing brand notoriety, or illuminating promoting techniques. Keywords Dataset, Sentiment analysis, Random Forest, Challenges and Future, Stopford’s, NLP etc.

Insights from Dynamic Pricing Scenarios for Multiple-generation Product Lines with an Agent-based Model using Text Mining and Sentiment Analysis

Corporations must constantly upgrade and improve their offerings due to changes in customer preferences. It is a common strategy for firms in technology-intensive markets to use online reviews as a source of product information to inform such changes. This user-generated information is valuable since it provides companies with valuable and low-cost input. In this paper, we propose an agent-based model for simulating potential cannibalization situations with respect to customer satisfaction throughout consecutive generations of a product line. The level of customer satisfaction is regarded as a parameter in the model, which is conceptualized to affect the product price. The proposed model provides insights into different pricing strategies regarding customer satisfaction levels affect the total lifecycle profitability of multiple-generation product lines, and how they can be used to assist organizations in developing appropriate dynamic pricing strategies.

Social Media Influence, Role, Challenges, and Effect on Medicine

Social media has grown exponentially as an open, social platform for communication and information sharing. By April 2020, there were 3.81 billion active users, fueled by increased internet and mobile phone access. A notable aspect of these platforms is their potential in public health initiatives, reaching large audiences and offering direct messaging capabilities. Social networks often exhibit higher engagement and retention rates than traditional web-based approaches, and their interactive nature may have a more significant impact. Social media has been utilized for healthrelated interventions, public health campaigns, medical education, disease outbreak tracking, and health research. It facilitates cost-effective, two-way communication between healthcare professionals and patients, enhancing current medical practices. However, the use of technologies and eHealth applications presents challenges, including establishing trust, adhering to regulations, and curating optimal content. The emotional content of user-generated information, especially during health crises, raises concerns about misinformation, stigma, and mental health issues such as cyberbullying and depression. The misuse of social media can harm personal relationships and reputations. In medical contexts, it requires careful handling of information accuracy and accessibility. Issues with compliance, trust, and patient privacy have impacted doctor-patient relationships. The development of standard protocols for health information dissemination and understanding personal health data usage in various cultural and social contexts remains a key area for further research and exploration.

A Perspective Study on Tweet Sentiment Analysis using Data Mining, Machine Learning and Deep Learning Paradigms

In recent years there has been an increase in interest in collecting and studying text from social networks, review websites, blogs, forums and other forms of user-generated information. The text offers a vast array of ideas from people of diverse profiles, including education, age and their perspectives, region of residence, on how they see goods and services, policy opinions, etc. The analysis of judgments, responses, and emotions drawn from texts is known as sentiment analysis. The sentiment categorization procedure establishes whether a text is subjective or objective, or whether it provokes both positive and negative responses. The most popular method of classification is based on polarity or orientation for accomplishing tweet sentiment analysis. In this paper, a detailed survey on various algorithms used for performing opinion mining, sentiment analysis, tweet sentiment analysis is discussed in detail. The study shows that text preprocessing, data mining, machine learning algorithm and deep learning paradigms plays a vital role in categorization of people’s feeling on a specific topic or a product. In this study, the existing challenges in optimizing the process of tweet sentiment analysis is also discussed and the suggestions for improving is also discussed.

Chinese Dianping App Users Perceptions of Inauthentic User-Generated Information on the Platform: Exploring Attitudes and Behaviors

Third-party review platforms have revolutionized consumer decision-making in the digital age, with (Dianping) serving as a notable example in China. Dianping, which was established in 2003 and offers user-generated content (UGC) on a variety of businesses, has emerged as a key player in the Chinese consumer experience. However, with its popularity, Dianping has also become a battleground for inauthentic information, including incentivized reviews and manipulated rankings. This research investigates Chinese Dianping app users perceptions of inauthentic information on the platform. This study investigates ten participants understanding of inauthentic content, their reactions to creating it, and the variables influencing their continued use of Dianping through semi-structured interviews. Participants recognize inauthentic content as containing elements of truth but with exaggeration and bias. They present contradictory attitudes toward generating this kind of content, motivated by individual benefits and explained cognitive dissonance. Despite concerns about authenticity, most participants maintain a level of trust in the platform due to its extensive content repository and a lack of superior alternatives. This studys findings are consistent with the Uses and Gratifications Theory, revealing that users prioritize platform gratifications over authenticity concerns. The fact that Dianping can meet users informational, convenience, and communicatory needs outweighs the existence of inauthentic information. Despite its limitations, this research explored the complex attitudes and behaviors of Dianping users in navigating a platform where misinformation is pervasive and offers useful insights into how users interact with online review platforms.

Blurred Lines in Allegations of Musical Intertextuality: A Response to Orosz

This commentary responds to Orosz’s recent EMR article, which presents research on the accuracy of user-generated information on the whosampled.com website. In general, we find Orosz’s main conclusions—that the accuracy of an entry tends to relate to the type of sample (direct or interpolated), how distinct the sample is, and the style of music—are convincing and well-supported by examples. That said, we believe Orosz’s ultimate appraisal of the web site as an invaluable resource to be contingent on the user. From the viewpoint of a music theorist such as Orosz, the web site may indeed be a beneficial resource. But for other users, especially students aspiring to create popular music, a lack of education about the legal issues surrounding the web site may foster a problematic perspective on music sampling.

Platform Rhetoric and Fan Labour as the Building Blocks of <em>LEGO Ideas</em>

Platform Rhetoric and Fan Labour as the Building Blocks of <em>LEGO Ideas</em>

MAPPING URBAN HERITAGE IMAGES WITH SOCIAL MEDIA DATA AND ARTIFICIAL INTELLIGENCE, A CASE STUDY IN TESTACCIO, ROME

Abstract. The UNESCO 2011 Recommendation on the Historic Urban Landscape promotes to map cultural significance of urban heritage from the perspectives of the general public in pursuit of social inclusion in heritage management. The user-generated information already available on social media platforms in the form of images, comments, and ratings can be considered a rich source for collecting data concerning the tourists’ image of destinations and their collective perception of urban cultural heritage. Considering the large amount of unstructured data, artificial intelligence (AI) can construct structured feature vectors therefrom and significantly aid the analysis and collation processes compared to the traditional manual approach for mapping public perception of cultural heritage. This paper presents an exploratory case study conducted in the area of Testaccio, Rome, showcasing the use of AI to map the perceived and narrated urban heritage images using social media data. An image-sharing platform, Flickr, is used to collect thousands of posts containing images and comments in the area, which are further analysed with pre-trained image recognition, natural language processing, and dimensionality reduction algorithms. Results as the urban heritage images are visualised, showing the most significant elements from a public perspective. Such a methodology provides an alternative perspective of viewing the urban heritage attributes as a collection of depicted and posted content. It can contribute as a tool for the documentation of collective attention for inclusive heritage management and local development planning during the designing and policy-making processes.

Insights in a city through the eyes of Airbnb reviews: Sensing urban characteristics from homestay guest experiences

There is a growing interest in deriving insights about cities from crowdsourced data. We advance the discourse by employing homestay guest experience to sense urban characteristics. We evaluate the relationship between subjective perceptions and objective indicators thanks to rich information in textual reviews that we posit reflect urban qualities. Next, we investigate dominant topics about urban characteristics in Airbnb reviews (transportation, greenery, amenities, safety, and noise) with natural language processing techniques, i.e. a rule-based dependency parsing method designed to extract relevant information. Then, we establish the associations between sentiments and proxies representing the physical patterns of urban areas. The multi-scale results of the experiments in three cities (London, Singapore, and NYC) suggest that reviews on homestay platforms reflect transportation convenience, amenities, sense of safety, and noise pollution. The correlation is stronger at a higher administrative division level, while the perception of people on safety is more sensitive at a more granular scale. Densities of transportation and amenities in nearby districts are more likely to be perceived similarly. Furthermore, the spatial distribution of perceptions is possibly affected by the morphology and development of a city, and the diversity of guests. This study reveals new possibilities for sensing urban characteristics through user-generated information and introduces a new application of accommodation reviews, which may help alleviate gaps in availability of data required for planning.

Classifying Scuba Diving Sites through Diver Reviews with a Web Scraping Based UTADIS Application

This research addresses the utilization of user-defined and web-based data for decision-making in recreational activities, focusing on the case of scuba diving, a globally significant recreational pursuit. While a wealth of user-generated information is available on the internet for various leisure activities, harnessing this data involves extensive data collection and organization efforts. Our proposed methodology involves the collection and organization of user reviews, both in verbal and quantitative forms, to create a pertinent dataset for applying multiple criteria decision methodologies to classify diving sites worldwide. An initial dataset containing over 14,000 diving sites worldwide is aggregated into 721 regions and these regions are classified using UTADIS methodology. The research showcases how user-generated review data can be transformed into valuable information, applying classification algorithms of multiple criteria decision analysis within the context of scuba diving. Furthermore, the proposed approach in this research holds the potential to serve as a model for leveraging user-generated data in decision-making processes across various service sectors such as hospitality and leisure that highly rely on customer experience by providing new insights on how more data-driven approaches can be utilized.

Credibility Evaluation of User-generated Content using Novel Multinomial Classification Technique

Awareness about the features of the internet, easy access to data using mobile, and affordable data facilities have caused a lot of traffic on the internet. Digitization came with a lot of opportunities and challenges as well. One of the important advantages of digitization is paperless transactions, and transparency in payment, while data privacy, fake news, and cyber-attacks are the evolving challenges. The extensive use of social media networks and e-commerce websites has caused a lot of user-generated information, misinformation, and disinformation on the Internet. The quality of information depends upon various stages (of information) like generation of information, medium of propagation, and consumption of information. Content being user-generated, information needs a quality assessment before consumption. The loss of information is also necessary to be examined by applying the machine learning approach as the volume of content is extremely huge. This research work focuses on novel multinomial classification (based on multinoulli distribution) techniques to determine the quality of the information in the given content. To evaluate the information content a single algorithm with some processing is not sufficient and various approaches are necessary to evaluate the quality of content. We propose a novel approach to calculate the bias, for which the Machine Learning model will be fitted appropriately to classify the content correctly. As an empirical study, rotten tomatoes’ movie review data set is used to apply the classification techniques. The accuracy of the system is evaluated using the ROC curve, confusion matrix, and MAP.

Optimize the prediction for particulate matter through RS IoT terminals

The result of manufacturing systems would be a mixture of liquid droplets, solid materials, & gas molecules that would be dispersed throughout the atmosphere. The assessment of fine particulate content in the surrounding atmosphere is of great significance for the protection of human health. Based on Air Quality Monitoring data sets, models of machine learning predictive for weather prediction particulate substance content in the atmospheric air were examined in this work. In this competitive atmosphere, proposed products to attract investigators & suit their demands have become critical. Although there are a variety of methods for recommending goods, collaborative filtering has emerged as an effective option. Furthermore, numerous evolution strategies could've been applied to get better outcomes in terms of prediction performance & cold flow difficulties. The current research investigates a modeling technique for user-generated information mixed with open big data, & also the necessary reference implementations & experimenting, to verify Recommendations Systems (RS) for novel citizen-centric systems. Furthermore, researchers investigate & test the performance difficulties with this Neo4j graph-based RS. It also assesses it as a valuable tool for big data applications.

Software Citation Workshop Results

Software Citation Workshop Results

When Self-Driving Fails: Evaluating Social Media Posts Regarding Problems and Misconceptions about Tesla’s FSD Mode

With the recent deployment of the latest generation of Tesla’s Full Self-Driving (FSD) mode, consumers are using semi-autonomous vehicles in both highway and residential driving for the first time. As a result, drivers are facing complex and unanticipated situations with an unproven technology, which is a central challenge for cooperative cognition. One way to support cooperative cognition in such situations is to inform and educate the user about potential limitations. Because these limitations are not always easily discovered, users have turned to the internet and social media to document their experiences, seek answers to questions they have, provide advice on features to others, and assist other drivers with less FSD experience. In this paper, we explore a novel approach to supporting cooperative cognition: Using social media posts can help characterize the limitations of the automation in order to get information about the limitations of the system and explanations and workarounds for how to deal with these limitations. Ultimately, our goal is to determine the kinds of problems being reported via social media that might be useful in helping users anticipate and develop a better mental model of an AI system that they rely on. To do so, we examine a corpus of social media posts about FSD problems to identify (1) the typical problems reported, (2) the kinds of explanations or answers provided by users, and (3) the feasibility of using such user-generated information to provide training and assistance for new drivers. The results reveal a number of limitations of the FSD system (e.g., lane-keeping and phantom braking) that may be anticipated by drivers, enabling them to predict and avoid the problems, thus allowing better mental models of the system and supporting cooperative cognition of the human-AI system in more situations.

Customer preference identification from hotel online reviews: A neural network based fine-grained sentiment analysis

As a kind of user-generated information, online reviews contain customers’ preferences for different aspects of hotels, which not only influence customers’ booking decisions but also help hotel managers to improve service quality of hotels timely. The key of deriving customers’ preferences from hotels’ online reviews is to identify fine-grained sentiment towards hotel attributes. In general, fine-grained sentiment analysis involves multiple fundamental tasks such as sentiment element extraction, aspect-opinion pair (i.e., AOP) identification and sentiment orientation analysis. However, existing fine-grained sentiment analysis methods cannot efficiently identify AOPs, especially when dealing with Chinese reviews. To this end, we construct an improved convolutional neural network (i.e., CNN) model, which can comprehensively utilize unstructured features and structured features, to improve the performance of AOP identification. We further propose a refined fine-grained sentiment analysis methodology to calculate accurate customer sentiment intensity value for each evaluated aspect rather than positive or negative sentiment, integrated with aspect term clustering algorithm, to identify customers’ specific preferences for different hotel attributes. Finally, to illustrate the reasonability and advantages of the proposed methodology, we conduct an empirical study with hotels’ online reviews crawled from Ctrip.com. Empirical results indicate that our proposed method can indeed improve the performance of AOP identification, and can effectively identify customer preferences from hotels’ online reviews. Furthermore, we find that customers show different preferences for different hotel attributes, and these vary across the types of customers.

Collective Intelligence during Emergency Egress: The Mechanisms Underlying Altruistic Information Exchange

Understanding the human factors governing effective information exchange is increasingly indispensable for the design of day to day human-computer systems. Moreover, effective information exchange becomes a matter of life or death during emergency egress. The complexity of an unknown environment and the unpredictable locations of hazards often prevent evacuees from identifying safe routes. Successful evacuations from locations impacted by fire or earthquakes may depend on user-generated information to increase the chance of collective survival. The present paper employed multi-user virtual reality experiments and an online survey to investigate the mechanisms underlying social influence and collective intelligence during emergencies. Our results demonstrate that information sharing helps to reduce evacuation time and trajectory length. Participants also shared more when given incentives or when there was a lack of knowledge in the public information pool. This work provides further indications of how collective intelligence can be promoted and deployed during emergencies.

Automatic Twitter Crime Prediction Using Hybrid Wavelet Convolutional Neural Network with World Cup Optimization

Social media are digitally mediated platforms that allow people to create and exchange content, professional interests, ideas, and other forms of expression through virtual networks. Users often utilize web-based programs on their PCs and laptops to visit social media sites, or they download programs that provide their devices social media capabilities. As users connect with these platforms, groups, organizations, and individuals can upload, co-create, discuss, engage in, and update self-curated or user-generated information. Although the platforms such as Facebook, Twitter, Instagram, etc., aid in the communication purposes, it also has some demerits like cyber-crime, hacking, etc. The growing number of crimes through these platforms needs to be deducted by predicting the crimes. For the crime prediction, the data acquired from Twitter is pre-processed for the data cleansing process. Later the features are extracted using various techniques like bag of words (BoW), Glove, term frequency-inverse document frequency (TF-IDF), and feature hashing. The feature selection is done using a modified tree growth algorithm (MTGA) and clustering is performed using the fuzzy manta ray foraging (FMRF). Finally, the crime detection is done using hybrid wavelet convolutional neural network with world cup organization (WCNN-WCO). The PYTHON tool is used for the implementation and the Twitter user dataset is used for analysis. The results showed that the proposed method outperforms the existing method in terms of precision, accuracy, [Formula: see text]1 measure, and recall.

Exploiting User Friendship Networks for User Identification across Social Networks

Identifying offline entities corresponding to multiple virtual accounts of users across social networks is crucial for the development of related fields, such as user recommendation system, network security, and user behavior pattern analysis. The data generated by users on multiple social networks has similarities. Thus, the concept of symmetry can be used to analyze user-generated information for user identification. In this paper, we propose a friendship networks-based user identification across social networks algorithm (FNUI), which performs the similarity of multi-hop neighbor nodes of a user to characterize the information redundancy in the friend networks fully. Subsequently, a gradient descent algorithm is used to optimize the contribution of the user’s multi-hop nodes in the user identification process. Ultimately, user identification is achieved in conjunction with the Gale–Shapley matching algorithm. Experimental results show that compared with baselines, such as friend relationship-based user identification (FRUI) and friendship learning-based user identification (FBI): (1) The contribution of single-hop neighbor nodes in the user identification process is higher than other multi-hop neighbor nodes; (2) The redundancy of information contained in multi-hop neighbor nodes has a more significant impact on user identification; (3) The precision rate, recall rate, comprehensive evaluation index (F1), and area under curve (AUC) of user identification have been improved.

Reducing the Intrusiveness of Energy and Water End-Use Disaggregation via Social Media and Users Interactions

Water end-use disaggregation from smart-metered water consumption data is key to design water demand management strategies. Yet, despite large investments for smart metering networks, few water utilities are actually exploiting the potential of end-use-based consumer profiling. This is mostly due to the intrusiveness of existing disaggregation algorithms, which require intensive and time consuming human interaction for extracting appliance-specific information and estimating the end-use patterns. We propose to explore the potential for social media in facilitating the interactions between water utilities and water users in order to collect consumption diaries. The evaluation of the value of this user-generated information is performed by using the collected diaries as input for a disaggregation algorithm, which is shown to attain acceptable performance in reproducing the end-use patterns.

Preface

Social media analysis can play a key role in providing insights into people's activities, opinions and day-to-day lives. When they are geolocated, these user-generated information streams become a unique opportunity to understand the rhythms and tenors of a city and its citizens. By applying computational, social science, and humanities methods to social media data such as photos, tweets and check-ins, researchers are now beginning to conceive of new methodological and theoretical frameworks not only to extract local insights but, more importantly, to better understand cities and their residents. This workshop aims to understand the various ways in which social media data can be used to produce knowledge about cities that supports citizen engagement.