Highly engaging events reveal semantic and temporal compression in online community discourse.

Citizen participation (CP) aims to reinforce the engagement of citizens in decision-making processes about significant choices affecting their cities and communities. With the emergence of the web-based crowdsourcing model, participants have become more involved in electronic participation processes. However, according to the literature, CP processes are in some cases, disconnected from citizens' living context and lacking responsiveness. In this paper, we argue the relevance of context in citizen participation and we propose a conceptual model for opinion contextualization that is based on semantic, spatial and temporal dimensions. The contextualization aims to connect citizens' input to relevant contextual variables that would enhance the understanding of concerns and thus to increase participation processes responsiveness. In order to test the proposed approach, a qualitative analysis process was handled based on a random sample of public transportation data in a city in Canada. This study argues the relevance of considering spatial, temporal and semantic dimensions in citizen participation processes.

The Privacy Journal (2003), a print newsletter and Web site devoted to privacy matters, defines the present-day use of the word privacy as “the right of individuals to control the collection and use of personal information about themselves.” Similar definitions are provided by law specialists (Gavison, 1980; Warren & Brandies, 1890). The networked society changes the way in which privacy rights are defined, used and interpreted, because: a. The IT-enabled channels of communication change the rules of personal and commercial interaction; b. The participation in the networked society implies a diminishing of individual privacy rights. The fundamental principle of the networked society is information sharing and processing (Kling & Allen, 1996). Advances in computing technology—that represents the infrastructure of the networked society—make possible to collect, store, analyze, and retrieve personal information created in the process of participation. The manifestation and the protection of individual privacy rights represent the field of conflict between various disciplines and social events. The heterogeneous nature of this phenomenon is mirrored in this paper, which aims to present the complex nature of privacy rights in the context of the networked society. The study proposes a negotiating model of online privacy rights, and analyses the necessary conditions for the implementation of this model on the Internet. The new economy is redefined on the basis of information entrepreneurism (Kling & Allen, 1996; Zwick & Dholakia, 1999). This cultural paradigm emphasizes the use of data-intensive analysis techniques for designing and implementing effective marketing and management strategies. This has as a direct consequence the use of an information superpanopticon–a concept derived from Foucault’s panopticon, a system of perfect surveillance and control. Online privacy is a major concern for Internet users (Ackerman, Cranor, & Reagle, 1999). For the individual Internet user, the privacy threats fall into two main categories: a. Web tracking devices that collect information about the online behavior of the user (e.g., cookies); b. The misuse of the personal information provided by the online user in exchange of specific benefits: increased personalization, Web group membership, etc. The databases, intelligent agents and tracking devices are surrounding the Internet users with a Web of surveillance, which is often hidden and unknown to the users. The surveillance is initiated by the simple act of presence on the Internet. Specialized software applications, such as cookies are tracking the online behavior of Internet users, feeding the data into databases, which create and permanently update a profile of online consumers. These profiles are then used for segmenting the market and targeting the most profitable consumers. A company can use cookies for various valid reasons: security, personalization, marketing, customer service, etc., however, there is an important distinction between cookies, which are active only within a specific Web site, and the ones that can track the user’s activity across unrelated Web sites. Recently, some aggregator networks have deployed hidden ‘pixel beacon’ technology that allows ad-serving companies to connect unrelated sites and overcome the site-specific nature of traditional cookies (Mabley, 2000). Additionally, some companies are now connecting this aggregated data with offline demographic and credit card data. Eventually, these resulting databases can be used or sold as powerful marketing tools. Exercising control of information, after it was voluntarily released, presents another critical problem. The misuse of personal information covers many possible aspects, which can be defined as any use which is not explicitly defined in the company’s privacy disclaimer, or which is not approved by the informed customer. For example, in 2000, Toysurus.com was subject to intense debate and controversy, when it was discovered that shoppers’ personal information was transferred through an unmarked Internet channel to a data processing firm, for analysis and aggregation. This operation was not disclosed in the company’s privacy disclaimer, and therefore, online customers were not aware of it. Regulators and legislators have addressed the controversial privacy issue quite differently across the world (Nakra, 2001). The USA, the largest world’s financial and Internet market, has not yet adopted a national, standard-setting privacy law (Jarvis, 2001). U.S. privacy statutes have primarily focused so far on protecting consumers’ financial data, health information, and children’s personal information (Desai, Richards, & Desai, 2003; Frye, 2001). In comparison with the American official opinion that online privacy protection is a matter of voluntary self-regulation by market-driven companies, the Europeans consider that it is more effective to enforce specific legislation regarding this issue. The current European approach is based on three basic tenets: 1. Individuals have the right to access any data relating to them and have it kept accurate and up-to-date; 2. Data cannot be retained for longer than the purpose for which it was obtained, nor used or disclosed “in a matter incompatible with that purpose”, and must be kept only for “lawful purposes”; 3. Those who control data have “a special duty of care” in relation to the individuals whose data they keep. Data commissioners oversee these rights in each European country and require most “data controllers”—people who handle data—to register with them to track what information is being collected and where. They are charged also with investigating all complaints from citizens. These principles have been incorporated in the European Data Directive, which came into effect in 1998, and more recently, in the European Directive on Privacy and Electronic Communications, adopted in 2002. Despite these legislative efforts, it is not yet clear how effective are the measures implemented by EU States. The direct involvement of governmental institutions can be considered as a form of censorship that can undermine the freedom and the flexibility of the Internet domain.

Online digital systems are driving the growth of economies, but they have also given rise to problematic online user behavior. Online shady practices are a threat to social cohesion, yet they persist and evade legal detection. To promote a healthy and inclusive digital environment, there is a need for further research to understand the interplay between culture, legislation, and online user behavior. This study aims to examine the interrelations between socio-cultural settings, cyber policies, and online user behavior. It explores the effectiveness of current policy measures and posits possible ways for promoting a healthy and inclusive digital environment. The study employs a narrative review of literature to understand the interplay between culture, legislation, and online user behavior. The findings highlight the influence of cultural settings on online user behavior and the interconnectedness of legislation, culture, and online interactions. The emphasis on the need for context-specific approaches and inclusive policymaking methodologies to foster a healthy and inclusive digital environment were the knowledge gaps identified that warrant further research. Understanding the interplay between cultural settings, legislation, and online user behavior provides valuable insights for practitioners and researchers to look more into a culturally sensitive safe digital space.

Suicidal ideation detection in online social networks is an emerging research area with major challenges. Recent research has shown that the publicly available information, spread across social media platforms, holds valuable indicators for effectively detecting individuals with suicidal intentions. The key challenge of suicide prevention is understanding and detecting the complex risk factors and warning signs that may precipitate the event. In this paper, we present a new approach that uses the social media platform Twitter to quantify suicide warning signs for individuals and to detect posts containing suicide-related content. The main originality of this approach is the automatic identification of sudden changes in a user's online behavior. To detect such changes, we combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for change detection in data streams. Experiments show that our text-scoring approach effectively captures warning signs in text compared to traditional machine learning classifiers. Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.

This report presents the results of a pilot study on the online behavioral habits of 46 internet users; 26 of whom self-identified as having a visual impairment (either blind or low vision). While significant research exists which documents the degree of difficulty that users with visual impairments have in interacting with the Web relative to the sighted, few have addressed the degree to which this usability disparity impacts online behavior; information seeking and online exploratory behaviors especially. Fewer still have addressed this usability disparity within the context of distinct website types; i.e. are usability issues more pronounced with certain categories of websites as opposed to others? This pilot study was effective both in exploring these issues and in identifying the accessibility of online social networks as a primary topic of investigation with respect to the formal study that is to follow.

User attributes including online behavior history and demographic information are the keys to decide whether a user is the right audience for an advertisement. When a user visits a website, the website generally plugs a browser cookie string (`bcookie' for short). The bcookie is then used as an identifier to collect the user's online behavior, as well as the joint key to link user profile attributes, such as demographic information and browsing history. However, the same users can have different bcookies across different browsers and devices. Moreover, bcookies can expire after some period, be cleared by browsers or users. This situation of bcookie discounting typically introduces both performance and delivery problems in online advertising since advertisers are hard to find the most receptive audiences based on the user profile information. In this paper, we try to tackle this problem by using an `assistant identifier' to find the linkage between different bcookies. For most of the Internet company, in addition to the bcookie information, there are always other identifiers such as IP address, user agent, OS type and version, etc., stored in the serving log data. Therefore, we propose an unified framework to link different bcookies from the same users according to those assistant identifiers. Specifically, our proposed method first constructs a bipartite graph with linkages between the assistant identifiers and the bcookies. Next all attributes associated with each bcookie are propagated along the graph using the state-of-the-art random walk model. Offline comparative experimental studies are conducted to confirm that by enriching the bcookie attributes we can recover 20% more online users whose bcookie information is lost, which is greatly helpful to delivery more budget spending with a little loss in precision of predicting converted users. On-product evaluation further confirms the effectiveness of the proposed method.

Understanding people's online behaviour has traditionally been a field of interest of commercial research agencies. However, academic researchers in a variety of fields are interested in the same type of data to gain insights in the Web behaviour of users. Digital Humanities scholars interested in the use of digital collections are, e.g., interested in the navigation paths of users to these collections. In our case we wanted (1) to analyse the way news consumers visit news websites and (2) understand how these websites fit in their daily news consumption patterns. Until now most common applied scholarly research methods to analyse online user behaviour focus on analyses of log files provided by website owners or recalled user behaviour by survey, diary, or interview methods. Only recently scholars started to experiment with gathering real-world data of Web behaviour by monitoring a group of respondents. In this article we describe the set-up of 'The Newstracker', a tool that primarily allowed us to analyse online news consumption of a group of young Dutch news users on their desktop and laptop computers. We demonstrate the workflow of the Newstracker and how we designed the data collection and pre-processing phase. By reflecting on the technical, methodological, and analytical challenges we encountered, we illustrate the potential of online monitoring tools such as the Newstracker. We end our article with discussing its limitations by stressing the need for a multimethod study design when aiming not only to analyse but also to understand online user behaviour.

This study is about the new expressive techniques for promotional videos for culture contents and aims to propose a new design model for making effective promotional videos using augmented reality technology and E-museum concepts. Augmented reality is one of the up-rising intelligent information technology, but has been barely used for promotional videos. However the technology enables the interaction between physical objects in the real world and virtual image though digital devices. Augmented reality opens the door for connecting real and virtual world, and invites humans to the virtual environment. There are growing numbers attempt to move off-line culture contents into on-line space. Considering the connection between culture contents and digital environment, more studies about the convergence is necessary in the cultural-art industry, especially for marketing business. Therefore, this study aims to take a brief look at augmented reality technology, and proposes the new experimental model and expressive techniques for cultural-art organizations’ promotional video, which set the experiential learning environment, and enables consumers to take contents actively.

Compressive spectral video (CSV) imagers have become a high-interest research topic in the computer vision community. This increasing popularity is due to the CSV imagers allow recovering a four-dimensional tensor from a reduced set of two-dimensional compressed measurements. However, CSV approaches have been limited to compress the spectral dimension for each frame, i.e., temporal compression is not developed. Alternative approaches are the dual-arm systems that rely on a compressive temporal and a spectral imager that fused adjacents measurements to obtain the four-dimensional datacube. Therefore, the development of a CSV imager that allows the reconstruction of the spectral and temporal dimension in a single compressed measurement is highly desired. This work proposes a coded aperture spectral compressive video methodology that allows encoding the spectro-temporal dimensions by using an active binary codification element and a tunable filter. Through the proposed optical system both the spectral and temporal dimensions can be modulated over an integration time using only two optical elements. In order to resolve the inverse problem, an alternating direction multipliers method (ADMM) plug and play (PnP) reconstruction algorithm was modified to render the multidimensional data. Simulations show that the proposed sensing methodology allows spectral video reconstructions from a single compressed measurement. Moreover, simulations show reconstruction results up to 28 dB and 0.86 in PSNR and SSIM metrics, respectively, with compression rates of 99%.

College Network Center has accumulated large number of user's online behavior data. We tried to analyze these data in order to grasp the user surfing habits and potential relationship, and then summarize an effective strategy for user online management. First of all, we taken a university as the example, and collected the user online data to extract related fields for sampling. In addition, we proposed a figure to present the number of Internet users in different time. After that, we use k-means algorithm to cluster the records, improved the algorithm by considering the element of user information. Finally, we apply the improved algorithm on a dataset and get the conclusion.

This paper examines how Search engine optimization (SEO) and Pay-per-click (PPC) interact and complement each other. Search engine optimization is a slow but effective process that gives results and provides organic traffic, while Pay-per-click (PPC) gives quick results and generates potential conversions in a much shorter time frame. The paper looks at how two radically different marketing approaches would interact most effectively when they work together. We’ll also look at how search engines play a crucial role in targeting audiences by analyzing user behavior. Search engine optimization and Payper-click contribute to this process by collecting data on user behavior, keywords and above all by directing potential customers to products, services or information. Audiences are created and organized after the search engine has collected data from user behavior after SEO and Paid search have worked and collected data. Users often utilize Search Engines to find information, the best price, the best shop, and so on. Customers, reach what they want with keywords. This paper best practices and how both approaches would work for a given business. It paper also presents different viewpoints of different authors and gives positive or negative opinions on the topic. Including what are the best practices for using PPC and SEO and exploring how they would work best for businesses and how research results show that the combination of the two methods leads to increased conversions and increased ROI. This research also provides recommendations for the development of Digital Marketing strategies tailored to individual business needs that have been proven to significantly improve the online presence of a given business in the online space.

Traditionally, the description of the scale of geographical information is mainly concerned with spatiality and its temporal character and semantic character is ignored. With a few of new techniques and way such as 3D geographical information system, temporal geographical information system, virtual geographical environment, electronic map appears, the traditional description of scale of geographical information shows its deficiencies to make certain of every aspect of it. At first, in this paper we try to extend the connotation and the dimension of scale of geographical information to full the need of describing the every aspects of it, and give three-tiered conceptualization of its scale as: kinds of scale; dimensions of scale; components of scale. The dimension of scale of geographical information is consisted of spatial dimension, temporal dimension and semantic dimension. The classifications of scale of geographical information are of phenomena scale, measure scale and token scale. The components of scale of geographical information are of extension, granularity (resolution) and interval. It is through the Cartesian product of kinds of scale and components of scale can we define the particulars of every aspects of scale of geographical information. At last in this paper the transform of scale of geographical information and issue of scale choice is discussed.

The use of social network sites helps people to make and maintain social ties accumulating social capital, which is increasingly important for individual success. There is a wide variation in the amount and structure of online ties, and to some extent this variation is contingent on specific online user behaviors which are to date under-researched. In this work, we examine an entire city-bounded friendship network (N = 194,601) extracted from VK social network site to explore how specific online user behaviors are related to structural social capital in a network of geographically proximate ties. Social network analysis was used to evaluate individual social capital as a network asset, and multiple regression analysis–to determine and estimate the effects of online user behaviors on social capital. The analysis reveals that the graph is both clustered and highly centralized which suggests the presence of a hierarchical structure: a set of sub-communities united by city-level hubs. Against this background, membership in more online groups is positively associated with user’s brokerage in the location-bounded network. Additionally, the share of local friends, the number of received likes and the duration of SNS use are associated with social capital indicators. This contributes to the literature on the formation of online social capital, examined at the level of a large and geographically localized population.

Privacy preservation of online user behavior data is important aspect in data mining, as of late, the study of privacy preserving data mining or networking has been studied mainly due to the extensive proliferation of confidential data on the internet. As online social networking sites attract thousands of million people to use it every day for different purpose, due to this lots of data is being generated every day on internet. Online user behavior analysis and security is becoming increasingly important, as it offers valuable information which can be useful for research purpose or analyst to develop better e-commerce strategies. However, it raises significant privacy concern of users data. Data aggregation scheme is generally used for privacy concern. In this paper, for privacy preservation of multiple online users behavior data, we have studied and analyzed multiple techniques. The main objective behind this survey paper is to percepting the existing privacy preservation and analysis technique of user behavior data and to find a better techniques and algorithms as a way to offer excessive efficiency and scalability. Subsequently, several methods for the privacy preservation and analysis have been suggested to attain the highest relevance.

With the constant development of socialist modernization construction in our country, our country's computer information technology has made effective progress, and makes the world into an information age. People's production and life style experience a series of change. However, with the emergence of information diversity and multi-user mode, traditional computer information technology already can't satisfy people's needs, and the development and application of cloud computing technology come to the stage. This article focuses on analyzing the big data user behavior engine design under cloud computing technology, covering big data flow management, multi-user system design, and other aspects to explore the effect of the system test. In recent years, China's mobile Internet technology has made full development, which makes the Internet operators in our country face a new development opportunity and began from traffic management to flow management. The law of user behavior is analyzed to explore the real demand of market and the users. In order to fully meet the changing needs of users, operators must constantly develop and launch new products and strengthen the function of computer technology. Cloud computing technology is such kind of engine system which can satisfy the analysis and processing of huge amounts of data. RESEARCH ON CLOUD COMPUTING SYSTEM OVERALL DESIGN The overall architecture of cloud computing system This study mainly uses cloud computing technology’s huge amounts of data computing to set up complete mobile Internet data mining analysis system. Realize the analysis for Internet user behavior engine, and according to the user's preferences online habits and behavior, provide users with targeted personalized service and form a unified organic whole of data collection, analysis and service type and marketing strategy to improve enterprise's marketing efficiency. In addition, cloud computing system mainly realizes data collection with the help of FTP server, and makes distributed computing and data batch processing at the system interface. Large data shall be deposited in Hbase database. The system can not only realize mass data storage, but those unstructured data storage. Then through Hive integration layer and summary EIL processing, use MapReduce data analysis model to transmit the processing result into the database. The system’s overall structure is shown in Figure 1: 3rd International Conference on Materials Engineering, Manufacturing Technology and Control (ICMEMTC 2016) © 2016. The authors Published by Atlantis Press 1351 Figure 1 The overall architecture of cloud computing system System topology and function distribution System topology mainly refers that a server is taken as Hapdoop platform’s master node server, and the others are Hapdoop platform’s node servers. In general, node server can be dynamically extended according to the actual need, and the master node server not only assigns tasks and flow to node server, but also monitors the work execution of the node server. More than one node server participates to complete the task, and it can improve data processing efficiency. The master node server’s software running status is shown in Figure 2: Figure 2 Master node server function structure