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Intelligent analysis of user generated content (UGC) plays an important role in ensuring urban natural gas safety and controlling process risks. However, most existing analysis methods are single-task driven and ignore the spatio-temporal information of gas sensing data. To address these problems, we propose a Transformer-based cyber-physical social security system (CPSS) for UGC analysis. Specifically, this unified system integrates multiple tasks, i.e. quality assessment and control of gas data, security factors of user consumption, and spatio-temporal abnormal gas signal detection. In the developed Transformer-based model, a time-space cross-attention module is embedded for combining the long-range spatio-temporal dependences of gas data. Moreover, a feature memory block module is introduced for abnormal feature enhancement and high-level representation of gas quality. Experimental results on related gas datasets demonstrate that this Transformer-based method achieves state-of-the-art performance, and the security system significantly improves the safety factor of natural gas use in smart cities, providing a robust framework for risk management and safety enhancement. • A Transformer based spatiotemporal assessment model is designed based on our personally annotated gas user datasets. • A Time-Space Cross Attention (TSCA) module is designed for abnormal feature enhancement and high-level representation of gas quality. • Based on the proposed Transformer, a unified CPSS security system is designed for the gridded management of gas supply chain and the cultivation of security culture. • The proposed method achieves SOTA on the gas security datasets compared with previous spatiotemporal or sequential deep learning methods. [ABSTRACT FROM AUTHOR]

• Artificial intelligence logic is useful as a supplementary instrument. • Knowledge and information enhance decision-making. • Increased levels of knowledge of 3D-printed foods influence behaviour. • 3-D printed foods have a positive effect on resistance to new foods. 3D-printed foods remain narrowly understood by consumers, limiting their ability to make informed choices and potentially obscuring the broader reality of this market. This research aims to investigate the factors influencing consumers' knowledge, motivation, and intention to consume 3D foods. Leveraging Artificial Intelligence logic, we foreground consumers' opinions and sentiments about 3D-printed food preferences, drawing on empirical data from two surveys. The findings of sentiment analysis show that the level of knowledge is critical in forming consumer sentiments both, positive and negative. Survey 1 examines the variations in positive affect, negative affect, and behavioral intention toward 3D-printed foods, considering extensive and limited levels of knowledge adequacy. This investigation uses the Knowledge Attitude-Behaviour theoretical model. Survey 2 identifies relationships through Topic recognition and applies the approach-avoidance motivation theory to discern the connection between health choices. Motivation (approach)/(avoidance) for 3D-printed foods both, had a positive and significant effect on healthy food choices. While Motivation (avoidance) had a positive and significant effect on resistance to new foods, Motivation (approach) of 3-D foods was not supported. The findings represented the importance of motivational tendencies, such as approach and avoidance, in shaping decisions related to healthy eating and receptiveness to innovative food concepts. This research presents an opportunity for researchers and practitioners to deepen their understanding of food development and consumer trends in the 3D-printed food market. Relying solely on User-Generated Content for conclusions may be insufficient. Additional research methodologies and data sources are necessary for a comprehensive understanding of behavior and food preferences. [ABSTRACT FROM AUTHOR]

For information asymmetry in e-commerce platforms, question answering system (Q&A) and online comments are used to build trust relationship. Although online reviews play a significant role in reducing information asymmetry, information overload and distortion are also not negligible. In the study, text mining was used to extract various questions from Q&A system and online comments to obtain the content that consumers are most concerned about in online shopping, and the similarities and differences of the two mining results are compared. The results show that Q&A system has a great influence on users' decision making. The Q&A system is not only complementary to online comments, but also can provide validation for the information of online comments. [ABSTRACT FROM AUTHOR]

Migrant integration is a long drawn out process requiring synergies with various dimensions of life, rhyming with those of the host country. In this paper, we attempt to deconstruct the digital narratives of migrants to explore how they may lead to a meaningful assessment of their acculturation and consequent integration in their host societies'. Drawing on acculturation theory as a lens, we argue that migrants' use of social media creates a liminality that is synonymous to ambiguity and disorientation that may diminish through a composite adaptation of acculturation and ethnic identity. Our data evidence on social media use among migrants domiciled in major cities in Sweden suggest that social media-based interaction of migrants is not encouraging integration, while their digital proclivities tend to define their narratives of online ethnicity and their physical realities. Implications for migrant integration are presented. • Acculturation as a result of migrant use of social media can lead to multiple outcomes. • Migrants social media based activity can lead to isolation. • Social media based interactions support strengthening of networks of co-nationals. [ABSTRACT FROM AUTHOR]

The importance of perception through all the senses has been recognized in previous studies on landscape preference, but data on aural perception, as opposed to the visual, remains rare. We seek to bridge this gap by analyzing texts that describe more than 3.5 million georeferenced images, created by more than 12000 volunteers in the Geograph project. Our analysis commences by extracting and automatically disambiguating descriptions that potentially contain verbs and nouns of sound (e.g. rustle, bellow, echo, noise) and adjectives of sound intensity (e.g. deafening, quiet, vociferous). Using random forests we classify more than 8000 descriptions based on the type of sound emitter into geophony (e.g. rustling wind, bubbling waterfall), biophony (e.g. gulls calling, bellowing stag), anthrophony (e.g. roaring jets, rumbling traffic) and perceived absence of sound (e.g. not a sound can be heard) with a precision of 0.81. Further, we additionally classify these descriptions as negative, neutral and positive using an Opinion Lexicon and GloVe word embeddings. Our results show that sentiment classification gives an additional level of understanding of descriptions classified into different types of sound emitters. We see that geophony, biophony and anthrophony cannot be uniquely classified as positive or negative. Our results demonstrate how text can provide a valuable, complementary to field-based studies, source of spatially-referenced information about aural landscape perception. [ABSTRACT FROM AUTHOR]

To date, social media has been used predominantly by the pharmaceutical industry to market products and to gather feedback and comments on products from consumers, a process termed social listening . However, social media has only been used cautiously in the drug development cycle, mainly because of regulations, restrictions on engagement with patients, or a lack of guidelines for social media use from regulatory bodies. Despite this cautious approach, there is a clear drive, from both the industry and consumers, for increased patient participation in various stages of the drug development process. The authors use the example of HealthUnlocked, one of the world’s largest health networks, to illustrate the potential applications of online health communities as a means of increasing patient involvement at various stages of the drug development process. Having identified the willingness of the user population to be involved in research, numerous ways to engage users on the platform have been identified and explored. This commentary describes some of these approaches and reports how online health networks that encourage people to share their experiences in managing their health can, in turn, enable rapid patient engagement for clinical research within the constraints of industry regulation. [ABSTRACT FROM AUTHOR]

YouTube (owned by Google Inc.) is arguably among most popular social media platforms used by millions across the globe. It provides an ever-growing, unique and rich source of content which presents new opportunities and challenges for information discovery and analysis. It is pertinent to explore and understand a topic via YouTube content to discover interesting information about public opinions and sentiments. This paper presents an integrated framework to facilitate the acquisition, storage, management, processing, and visualization of relevant content with the objective to assist in such analysis. It not only collects a significant portion of content, relevant to a given topic, in short time but also offers tools for visual exploratory analysis such as; (i) temporal evolution, (ii) vocabulary network, (iii) authors relative popularity and influence (iv) categories and (v) user communities and influencers. The utility and effectiveness is demonstrated through content analysis of a famous YouTube entertainment topic, the “ Gangnam Style ”. [ABSTRACT FROM AUTHOR]

Describing current, past and future landscapes for inventory and policy making purposes requires classifications capturing variation in, for example, land use and land cover. Typical land cover classifications for such purposes result from a top-down process and rely on expert conceptualisations, and thus provide limited space for incorporating more widely held views of key landscape elements. In this paper we introduce the notion of spatial folksonomies, which we define as a tuple linking a vocabulary of landscape terms through authors and resources to locations. We demonstrate how spatial folksonomies can automatically be created for Switzerland using two text corpora: the Swiss Alpine Club's yearbook for the past 150 years and user generated content from a website describing a wide range of outdoor activities. The spatial folksonomies capture variation in space of the use of nouns describing 96 natural landscape terms (e.g. ridge, forest, mountain, etc.) and allow us to characterise regions and compute similarities. We compare our spatial folksonomies to two traditional land cover/land use classifications (CORINE and Arealstatistik) and demonstrate that despite their very different sources, the approaches capture landscape variation in broadly similar ways. However, our spatial folksonomies provide new insights into how landscapes are described, through for example variation in space, time and through the prism of different activities. We argue that our spatial folksonomies are a novel way of capturing variation closer to the bottom-up understandings of landscape for instance required to describe cultural ecosystem services. [ABSTRACT FROM AUTHOR]

Nowadays, community Question-Answering (cQA) sites are massive repositories for user-generated content, where members prompt questions expecting satisfactory answers from other members. However, in this dynamic, there is an intrinsic delay between the moment questions are posted to the arrival of acceptable responses. Therefore, cQA platforms have the pressing need for promoting unresolved questions to potential answerers and for taking advantage of resolved questions contained in their archives, whenever possible. This paper studies cQA services from the viewpoint of the time frame where their questions attract the interest of their community members. By drawing a parallel with temporal patterns of user interests in web search activity, we are able to define three main types of temporally anchored questions: trend or bursty, periodic and permanent. Then, by analyzing user click distributions to Yahoo! Answers pages across Yahoo! Search logs, we automatically acquired a set of 35,000 cQA questions labeled with one of these three temporal anchors. Accordingly, we show the practicality of this approach by means of human assessments; and by using this automatically acquired corpus for studying several classification models. Essentially, the proposed method was found to correlate well with these human judgements, and proven to be effective in building systems that automatically identify the temporal anchor of unseen cQA questions. In substance, our outcomes indicate that some contexts are strongly related to a particular temporal anchor. We believe that these anchors will contribute to the discrimination of resolved questions that are capable of being revitalized, as well as to foster the opportune participation in questions that generate enthusiasm only for a short time. [ABSTRACT FROM AUTHOR]

Consumers trust the Internet for advice. Online Social Networks (OSNs) are creating collective knowledge and becoming major information gathering sources among tourists when making travel decisions and purchasing travel-related products and services. The aim of the current study is to develop a theoretical model that tests the precursors of “intention to share knowledge” behaviors in the context of OSNs. Based on the previous literature, a theoretical model was developed and tested using Confirmatory Factor Analysis and Structural Equation Modeling with a sample of travel-related OSN users who had at least one year of online travel shopping experience. Study results show that both perceived ease of use and belief in integrity positively influence knowledge sharing behaviors. Utilitarian beliefs and subjective norms positively influence belief in integrity. This is one of the few research studies within this field and study results present clear theoretical and practical implications for the travel and tourism industry. [ABSTRACT FROM AUTHOR]

Psychological health, i.e., citizens' emotional and mental well-being, is one of the most neglected public health issues. Depression is the most common mental health issue and the leading cause of suicide and self-injurious behavior. Clinical diagnosis of these mental health issues is expensive and also ignored due to social stigma and lack of awareness. Nowadays, online social media has become the preferred mode of communication, which people use to express their thoughts, feelings, and emotions. Hence, user-generated content from online social media can be leveraged for non-clinical mental health assessment and surveillance. Conventional machine learning and NLP techniques have been used for the automated detection of mental health issues using social media data for a very long time now. However, the objective of our research is to study the applications of deep learning techniques for early detection and non-clinical, predictive diagnosis of depression, self-harm, and suicide ideation from online social network content only. To the best of our knowledge, we did not find any systematic literature review that studies the applications of deep learning techniques in this domain. In order to address this research gap, we conducted a systematic literature review (SLR) of 96 relevant research studies published until date that have applied deep learning techniques for detecting depression and suicide or self-harm behavior from social media content. Our work comprehensively covers state-of-the-art w.r.t. techniques, features, datasets, and performance metrics, which can be of great value to researchers. We enumerate all the available datasets in this domain and discuss their characteristics. We also discuss the research gaps, challenges, and future research directions for advancing & catalyzing research in this domain. To the best of our knowledge, our study is the only and the most recent survey for this domain covering the latest research published until date. Based on our learnings from this review, we have also proposed a framework for mental health surveillance. We believe the findings of our work will be beneficial for researchers working in this domain. • It is imperative to develop real-time, scalable, and inexpensive mental health assessment & surveillance systems for detection of mental health disorders. • Such systems can assist in the early, non-intrusive, non-clinical, predictive diagnosis of depression, self-harm, and suicide related risks. • These critical and most common mental health conditions can be detected by mining & analyzing user-generated content from online social networks. • This research presents a systematic literature review of various deep learning (neural network based) architectures proposed for the above complex computational linguistic task. • Past research for this domain has mainly focused on English language content from Twitter & Reddit only. • CNN, LSTM, BERT, and their variants are the preferred and most commonly used deep learning models for this research problem. • Majorly unimodal textual datasets have been utilized for training supervised deep learning models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

From a strategy perspective, the growth of social media accelerates the need for tourism organisations to constantly re-appraise their competitive strategies. This study contributes theoretically to the tourism performance literature by validating a new approach to examining the determinants of hotel performance. Drawing from and extending prior hotel determinants studies, this study uses artificial neural network model with ten input variables to investigate the relationships among user generated online reviews, hotel characteristics, and Revpar. The sample includes 235 Swiss hotels for the period 2008–2010, with 59,688 positive reviews from 69 online sources. The empirical findings reveal four hidden nodes that have a significant impact on RevPar. Three of these have negative impacts: room quality, positive regional review, hotel regional reputation, and regional room star rating has a positive impact. Further, the findings imply that there may be boundaries to reputational benefits for Swiss hotels. [ABSTRACT FROM AUTHOR]

Highlights: [•] This study focuses on the community member’s online socialization in UGC sites. [•] UGC quality gap between current and new members is important for socialization. [•] Member feedbacks play key roles for new member participation when the gap exists. [•] Informative-feedback is more effective in superior UGC sites due to learning effect. [•] Friendly-feedback is more effective in inferior UGC sites due to intimacy effect. [Copyright &y& Elsevier]

Abstract: Online services and applications extend the influencing opportunities of traditional word-of mouth (WOM). Unlike traditional word-of-mouth, the online environment allows for special features such as anonymity in user-generated content. Furthermore, the personality of online users affects their motivation when creating this content. The aim of this paper is to link specific online activities, i.e., the posting of product ratings and participation in discussions in online forums, with certain personality traits within an empirical study. The findings, based on an online survey with more than 16,900 completed questionnaires, indicate that opinion leaders in the online environment cannot be compared with traditional opinion leaders in terms of their articulation and personality structure. In regard to online activities with a high influencing potential, the results of moderated regression analyses show that persons with an introverted personality are more active as online opinion leaders due to the lack of social recognition they experience. The results have implications for how marketers should present incentive structures to address and integrate potential online opinion leaders, and how scholars should understand the role of opinion leaders in the online environment. [Copyright &y& Elsevier]

Abstract: Drawing on the literature about online source classification, source credibility, and attribution theory, this study examines how the source of a product review influences people’s product judgments. Results from a between-subjects experiment suggest that the perceived source of a message (the visible source) impacts how people evaluate actual reviewer (the original source) and product. Reviews made by regular Internet users (visible sources) lead to greater trust in the actual reviewer (the original source), compared to product reviews from product makers. Results further indicate that visible sources play a crucial role in helping people judge the credibility of online reviews. Particularly, the identity of a visible source is used to consider the intention of original source of the message, which in turn determines message persuasiveness. The authors conclude that evaluating the intentions of online reviewers is a critical antecedent to forming opinions about online reviews and products. [Copyright &y& Elsevier]

Abstract: Ethnopharmacological relevance and Aims: The traditional use of the Hallucinogenic sage, Salvia divinorum has been of ethnopharmalogical interest for some time. This plant, endemic to Oaxaca Mexico and traditionally used by the Mazatec, is now utilized worldwide for its psychoactive effects. This use demonstrates a novel use pattern which is distinctly different from Mazatec use. This study offers a new methodology to study emerging global plant use and assesses the users’ experience with it. The aim of this research was to develop a new methodology to collect and analyze archived data on the World Wide Web, specifically videos which depict Salvia divinorum use. Methods: The basis of the methodology for this project was unobtrusive observation which allows the researcher to observe without influencing the event which is being observed. Qualitative, ethnographic data was used in conjunction with quantitative meta data collected by a customized web crawler programed to archive YouTube™ data. Results: Using this methodology enabled us to understand reported uses and the users’ experiences as expressed on the World Wide Web. The main result of this research was the documentation of a distinct, novel use pattern of Salvia divinorum which has developed outside of Oaxaca; a use pattern which differs in a number of ways from traditional, Mazatec use. The majority of the YouTube™ videos analyzed were found to present indications of a positive Salvia divinorum experience. This result highlighted the contradiction between ethnographic data and what is reported by the media. Finally the representation of Salvia divinorum on YouTube™ (and by inference the WWW as a whole) is a growing phenomena. Conclusions: While anthropological and more specifically medico-anthropological research has, for many years, embraced the dynamics of cultures, until recently, ethnopharmalogical research has generally focused on ‘traditional’ plant use, failing to capture the dynamic elements of plant/human interaction and framing research in the past or as decontextualized largely descriptive reports. Global migration and urban environments formed a basis for looking at the interplay of continuity and change. Such cultural dynamics are exacerbated by the opportunities which the WWW offers. [Copyright &y& Elsevier]

Abstract: There is an increasing demand on citizens to participate in social network websites and to create and share their own user-generated content (UGC), such as photographs, videos, and blogs. So far, little is known about how elderly people respond to these new trends and master the techniques required. This paper reports on three studies that investigated elderly people’s user requirements related to consumption, sharing and co-creation of UGC in new media. The first study, conducted in Norway, identifies patterns of Internet usage, age differences, and participation in online communities and the consumption, sharing and co-creation of UGC on a macro level. The second study, conducted in Belgium, investigated the social requirements of elderly people on a group level. The third study, also conducted in Belgium, investigated user and context requirements on an individual level. The results of the first study show that the elderly rarely participate in online communities and share audio-visual UGC. However, they embrace some aspects of the new media and more often express themselves politically. The results of the second study show that the elderly are very motivated to contribute with UGC, given the right circumstances. The results of the third study show that it is important for elderly people that they be able to use the new technologies easily and identifies their worries about using them. [Copyright &y& Elsevier]

Tourists are increasingly using drones on vacation, but how they use them and the nature of the videos that they produce are not well understood. Conceptual advances in the study of tourist videography have produced a new model of these practices which is applied here to explain the nature of tourist videography with drones. An international sample of 351 vacation drone videos was subjected to content analysis, and an analysis of their metadata. The results show a significant variation in the content, technological and social practice of production of vacation drone videos depending on the type of video creator and, therefore, empirically validate and expand extant knowledge of drone videography as an emerging visual practice in tourism contexts. The findings establish that analysing the videos from the perspective of videography generates insights that are of value to destination management organizations and tourist businesses. We conclude that destination management organizations should see vacation drone videos as a new type of user-generated content for their destinations, as well as a potential source of innovative marketing ideas, and that they should engage more proactively with vacation drone videographers to maximise the impact of this opportunity. • Vacation drone videos are new type of user-generated content used for innovative destination marketing. • Content analysis of vacation drone videos supported by analysis of metadata. • The social dimension of tourism drone videos plays a more important role than the technological dimension. • Tourists and influencers, as drone vacation video creators, are more oriented to self-promotion. [ABSTRACT FROM AUTHOR]

Availability of User Generated Content and the development of Big Data and machine learning algorithms have paved the way to collecting and analysing great volumes of data. We scan imagery data from traveling-related posts on Instagram to identify the key features of the destination image and of its dynamics. Specifically, we exploit a newly introduced Visual Object Recognition tool (Google Cloud Vision) to convert into textual labels the content of about 860,000 travel-related pictures posted on Instagram in Summer 2019 for several European islands. The output, a vector of labels' frequencies on a very fine-grained scale, is used to proxy the destination image at different points in time. We then introduce the Index of Distance in Destination Image, a metric built on the pictures' labels ranking, and aimed at providing a quantitative measure of (dis)similarity between destination images. We show that the analysis of labels and the index are fit to compare destinations cross-sectionally and over time, providing a useful tool for researchers, marketers and DMOs. We also deliver evidence on how external shocks (like extreme events linked to climate change) or the organization of events modify the cognitive sphere of the destination image, with repercussions on activities undertaken by tourists and relevant implications for local policies. • A Visual Object Recognition tool is applied to Instagram pictures posted by tourists. • The output is a vector of labels frequency for each destination and point in time. • An Index of Distance in Destination Images is proposed and applied to the output. • The metrics allows the investigation of differences and similarities across destinations. • Variation over time in destination image triggered by events is also found. [ABSTRACT FROM AUTHOR]

In his classic book "The Image of the City" Kevin Lynch used empirical work to show how different elements of the city were perceived: such as paths, landmarks, districts, edges, and nodes. Streets, by providing paths from which cities can be experienced, were argued to be one of the key elements of cities. Despite this long standing empirical basis, and the importance of Lynch's model in policy associated areas such as planning, work with user generated content has largely ignored these ideas. In this paper, we address this gap, using streets to aggregate filtered user generated content related to more than 1 million images and 60,000 individuals and explore similarity between more than 3000 streets in London across three dimensions: user behaviour, time and semantics. To perform our study we used two different sources of user generated content: (1) a collection of metadata attached to Flickr images and (2) street network of London from OpenStreetMap. We first explore global patterns in the distinctiveness and spatial autocorrelation of similarity using our three dimensions, establishing that the semantic and user dimensions in particular allow us to explore the city in different ways. We then used a Processing tool to interactively explore individual patterns of similarity across these four dimensions simultaneously, presenting results here for four selected and contrasting locations in London. Before drilling into the data to interpret in more detail, the identified patterns demonstrate that streets are natural units capturing perception of cities not only as paths but also through the emergence of other elements of the city proposed by Lynch including districts, landmarks and edges. Our approach also demonstrates how user generated content can be captured, allowing bottom-up perception from citizens to flow into a representation. • Capturing Lynch's idea of "The Image of the City" through user generated content • Street segments as a natural unit to organise image metadata to reduce data aggregating issues • Emerging elements of the city by combining different place dimensions capturing semantic similarity, user behaviour and temporal patterns • Applying different ranges of filtering methods to explore different place characteristics • Exploring perceptions of a city through not only paths, but through the emergence of districts, landmarks and even edges [ABSTRACT FROM AUTHOR]

The involvement of Information and Communication Technologies (ICT) has become essential for daily lives because their usages are broadly acceptable to meet the requirements of the modern era. The present communication architecture is depending on an out paradigm due to which similar data needs to send several times over the Internet. Therefore, global communication technologies are facing several critical issues related to modern ICT-based technologies such as link load, network congestion, unnecessary usage of resources, and server load. Recently, a new cache-based communication technology called Named Data Networking (NDN) emerged to overcome these issues. In NDN, network routers are equipped with a cache that provides storage to the disseminated content for caching at intermediate routers to fulfill the subsequent consumers' requirements. The caching strategy modifies communication services. Therefore, in this study, an innovative cache deployment strategy named as Most Interested Content Caching (MICC) is proposed to enhance the content dissemination by caching the transmitted content at appropriate locations. The MICC extensively and comparatively is evaluated by comparing its performance with existing NDN-based caching mechanisms. Hence, MICC is performed better with regards to the cache hit, stretch, eviction, and diversity. Consequently, the MICC will be efficient in enhancing the ICT-based communications approaches. [ABSTRACT FROM AUTHOR]

• Monitoring green infrastructure planning with Foursquare's user-generated content. • Linking green infrastructure elements and Foursquare data for policy design. • Foursquare insights on user perceptions over green infrastructure elements. • Planning green infrastructure considering social preferences and perceptions. • Identifying socially meaningful spaces for improving planning strategies. The design of efficient Green Infrastructure —GI— systems is a key issue to achieve sustainable development city planning goals in the twenty-first century. This study's main contribution is the identification of potential GI elements to better align the environmental, social and economic perspectives in the GI design by including information about the use, activities, preferences and presence of people. To achieve this, user generated content from Location Based Social Network —LBSN— Foursquare is used as a complementary data source. This involved the construction of an interdisciplinary correlation framework, between the Landscape Ecology principles and the GI elements, that included the classification of Foursquare data into grouping types. The classification considered the potential role of Foursquare venues in the GI network. Valencia City in Spain served as an illustrative case study to test the validity of the proposed method. The results suggest that Foursquare can provide a valuable insight on user perceptions of potential GI elements. Moreover, the findings indicate that user generated content from LBSNs like Foursquare can serve as a complementary tool for analysing the dynamics of urban outdoor spaces to assess GI network, thereby facilitating more effective urban planning and contributing to the social sustainability of the city. [ABSTRACT FROM AUTHOR]

The millennial generation plays a leading role in today's connected world in which exists a confluence of numerous technologies and the internet in science, economy and innovation. This study aimed to identify the key factors that characterize the millennial generation within the online chatter on Twitter using an innovative approach. To this end, we analyzed the user generated content (UGC) in the social network (SNS) Twitter using a three-steps knowledge-based method for information management. In order to develop this method, we first used latent Dirichlet allocation (LDA), a state-of-the-art thematic modeling tool that works with Python, to analyze topics in our database. The data were collected by extracting tweets with the hashtag #Millennial, #Millennials and #MillennialGeneration on Twitter (n = 35,401 tweets). Secondly, sentiment analysis with a support vector machine (SVM) algorithm was also developed using machine-learning. Applying this method to the LDA results resulted in the categorization of the topics into those that contained negative, positive and neutral sentiments. Thirdly, in order to gather the final results, data text mining techniques were used. The negative factors that characterize the behavior of this generation are depression, loneliness and real-world relationship. The positive factors are body image, self-expression, travelers and digital life and the neutral factors are self-identity and anxiety. Practical implications can be used by public actors, companies or policy makers that are focused on the millennial generation as a target. The study has important theoretical applications as the topics discovered can be used to test quantitative models based on the findings and insights extracted from the UGC sample. [ABSTRACT FROM AUTHOR]