Social information landscapes: automated mapping of large multimodal, longitudinal social networks

Purpose – This article presents a Big Data solution as a methodological approach to the automated collection, cleaning, collation and mapping of multimodal, longitudinal datasets from social media. The article constructs Social Information Landscapes. Design/methodology/approach – The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages and other forms of media, so long as a formal data structure proposed here can be constructed. Findings – The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on Web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g., centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating and mapping such datasets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem. Research limitations/implications – The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this article is to demonstrate the feasibility of using automated approaches for acquiring massive, connected datasets for academic inquiry in the social sciences. Practical implications – The methods presented in this article, and the Big Data architecture presented here have great practical values to individuals and institutions which have low budgets. The software-hardward integrated architecture uses open source software, and the social information landscapes mapping algorithms are not difficult to implement. Originality/value – The majority of research in the literatures uses traditional approach for collecting social networks data. The traditional approach is slow, tedious and does not yield a large enough sample for the data to be significant for analysis. Whilst traditional approach collects only a small percentage of data, the original methods presented could possibility collect entire datasets in social media due to its scalability and automated mapping techniques

Incorporation of urban differences in Tokyo, Mexico City, and Los Angeles

Reinvestment and intensification are common processes in many urban areas across the world. These transformations are often analyzed with concepts such as ‘urban regeneration’, ‘urban renaissance’, or ‘gentrification’. However, in analyzing Shimokitazawa (Tokyo), Centro Histórico (Mexico City), and Downtown Los Angeles, we realized that these concepts do not fully grasp the qualitative changes of everyday life and the contradictory character of the urbanization processes we observed. They do not take into consideration the far-reaching effects of these processes, and particularly do not address the underlying key question: how is urban value produced? Therefore, we have chosen a different analytical entry point to these transformations, by focusing on the production, reproduction, and incorporation of the intrinsic qualities of the urban. We found Lefebvre’s concept of ‘urban differences’ and Williams’ concept of ‘incorporation’ particularly useful for analyzing our empirical results. In this contribution, we compare the ‘incorporation of urban differences’ in the three case study areas and offer this concept for further discussions and applications

Predicting Medication Prescription Rankings with Medication Relation Network

Medication prescription rankings and demands prediction could benefit both medication consumers and pharmaceutical companies from various aspects. Our study predicts the medication prescription rankings focusing on patients’ medication switch and combination behavior, which is an innovative genre of medication knowledge that could be learned from unstructured patient generated contents. We first construct two supervised machine learning systems for medication references identification and medication relations classification from unstructured patient’s reviews. We further map the medication switch and combination relations into directed and undirected networks respectively. An adjusted transition in and out (ATIO) system is proposed for medication prescription rankings prediction. The proposed system demonstrates the highest positive correlation with actual medication prescription amounts comparing to other network-based measures. In order to predict the prescription demand changes, we compare four predictive regression models. The model incorporated the network-based measure from ATIO system achieve the lowest mean square errors

The Customer Engagement Approaches of Influential Entrepreneurship : Based on business related Customer Engagement approaches emerged on Weibo

This study focuses on an emerging field of Influential Entrepreneurship to research the adoption of Customer Engagement Approaches as effective business strategies in China context. The aim is to establish an interactive framework combining theoretical findings and empirical evidence to illustrate the Customer Engagement Approaches enacted by Influential Entrepreneurs. The primary data is collected from expert Interviewees, and raw data is later interpreted, categorized, and grouped with thematic networks to display the logic and relationship of different terms. An interactive framework of Customer Engagement Approaches by Influential Entrepreneurs is put forward, and the framework sheds lights both on theoretical contributions and practical implications. Suggestions for future researches are discussed in the end to provide scholars with academic indications. The study confirms that Customer Engagement Approaches are vigorously employed by Influential Entrepreneurs as efficient business strategies. Influential position themselves as knowledgeable, informative, and communicative figures with commonality and attainability so as to establish profit-oriented intimate relationship via Customer Engagement Approaches. Customer Engagement approaches vary from case to case, and approaches include commoditization of contents production, management of persona, display of friendliness, presentation of perceived authenticity and intimacy, mostly through mixed social media related activities with a supplement in offline events. In this case, Influential Entrepreneurs exploit customers¡¯ desire from multiple perspectives and trigger contagion effects, leading to mutually amplified effects in economic returns, popularity, business opportunities, and communication effects. Specifically, Influential Entrepreneurs deems tangible value triggered interaction as temporary, while they consider relationship-oriented interaction as profoundly effective. Multi-channelling effects of social media platforms are particularly added to the scaling up of customer base. Influential endorsements are charged with emotional premium, which is similar with corporate charged brand premium

Capital Ruptures: Economies of Crisis and Urban Space in Javier Moreno's 2020

This essay asserts that the aftermath of the 2008 global economic crisis has re-focused scholarship onto capitalism’s tendency towards what David Harvey calls “creative destruction,” where space is continually destroyed and reproduced to serve the interests of capital. Following Harvey, my essay investigates the representation of space in relation to economic crisis in Javier Moreno’s novel 2020. I contend that in 2020, excess non-“places”—specifically, taxis, supermarkets, and airports—intrinsic to late capitalist society transform into sites of solidarity and social transformation as capital relentlessly degrades proper “places” of the urban landscape. Thus, the essay critically examines depictions of spaces, places, and non-places in 2020 to argue that Moreno "capitalizes" upon the political economy’s contradictions to represent the city’s supposedly unhistorical, asocial, excess “non-places” as sites that subvert neoliberal principles in a futuristic Madrid plagued by unfathomable crisis. This line of inquiry ultimately leads me to envision the novel in crisis as a virtual commons that encourages dialogue and cultural critique

Graph based Anomaly Detection and Description: A Survey

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised vs. (semi-)supervised approaches, for static vs. dynamic graphs, for attributed vs. plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly attribution and highlight the major techniques that facilitate digging out the root cause, or the ‘why’, of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field

Eating the view : navigating the food rrisis through cooperative food communities in La Pau

Food communities, also referred to as food co-ops or consumer cooperatives, represent collectives of individuals united with the common goal of jointly procuring and managing the sourcing of various food and related products. These communities distinguish themselves through their shared values and objectives that revolve around sustainable, ethical, and health-conscious food consumption practices. Members of food communities often engage in collaborative efforts to endorse local producers, curtail their ecological footprint, and secure access to high-quality, frequently organic, food items. The study seeks to examine whether contemporary food communities mirror the consumption patterns of past organic food societies. To accomplish this, a comprehensive analysis was undertaken, tracing the historical roots of the cooperative movement up to the present day. The primary areas of investigation encompassed the size and legal framework of these communities, their proximity relationships with agroecological suppliers, and their governance models. By scrutinizing various case studies and diverse types of food communities, the study aimed to discern the optimal equilibrium between size, governance models, and the urban infrastructure necessary for the establishment of such initiatives. The objective is to demonstrate that while distinct models may function effectively within their specific contexts, amalgamating key components could lead to the creation of more efficient and impactful communities. As a pilot area, the neighborhood of La Pau in Barcelona, Spain, was utilized to test the hypothesis, particularly in light of the "15-minute city" concept. This approach illuminated the neighborhood's disconnection from the broader territorial context. Given Barcelona's rich history of cooperative movements and its well-established, evenly distributed municipal markets, the study strives to synthesize various aspects of food communities into a vision for a Cooperative Market model. The study also delineates potential governance pathways that encompass considerations of ownership and investment. The installation of one or more food communities, utilizing profits for reinvestment in a more sustainable neighborhood, is anticipated to mitigate food poverty, enhance social cohesion, and elevate overall living standards within the community