Online User Activity Research Articles

Social Media as Lieux for the Convergence of Collective Trajectories of Holocaust Memory—A Study of Online Users in Germany and Italy

By articulating a shared victimhood or guilt or responsibility, memorial museums are designed as ethical projects that encourage visitors to learn from the past to build a “better future”. In contemporary Europe, Holocaust memorials and museums constitute a trajectory of remembrance about public Holocaust memory that consolidates political legitimacy and articulates national narratives of the legacy of WWII. In parallel, increasing adoption and spread of digital technologies have resulted in a convergence and a globalisation of themes and user interests associated with Holocaust memory. The purpose of this study is to investigate the perspectives and interests of users of the social media profiles of a group of Holocaust museums and memorials in Germany and Italy. Using a primarily quantitative approach, the study sought to understand the motivations, interests, and online activities of users of nine Holocaust museums and memorials. While national narratives regarding public policy continue to diverge in the two countries, users express a convergence of interests and motivations when using these social media profiles. This dual venture of Holocaust memory is a complex yet powerful example of how the globalisation of digital media is playing an increasingly significant role in European contemporary society.

Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

Nowadays, commercial transactions and customer reviews are part of human life and various business applications. The technologies create a great impact on online user reviews and activities, affecting the business process. Customer reviews and ratings are more helpful to the new customer to purchase the product, but the fake reviews completely affect the business. The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information. Therefore, in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity. Here, Amazon Product Kaggle dataset information is utilized for investigating the customer review. The collected information is analyzed and processed by batch normalized capsule networks (NCN). The network explores the user reviews according to product details, time, price purchasing factors, etc., ensuring product quality and ratings. Then effective recommendation system is developed using a butterfly optimized matrix factorization filtering approach. Then the system’s efficiency is evaluated using the Rand Index, Dunn index, accuracy, and error rate.

MESOECONOMIC MARKET ANALYSIS BASED ON SELECTED BIG DATA TOOLS – THE CASE OF WOOD MARKET TRENDS IN POLAND UNDER ECONOMIC UNCERTAINTY

The paper discusses selected trend changes in the Polish wood-based products market under conditions of economic uncertainty. From a mesoeconomic approach, using Big Data resources and online e-commerce tools, the wood market was analyzed from the perspective of demand and online user activity. In terms of time scope, the research covered the period 2019-2021, including the pandemic year. Secondary data based on Google Ads and Google Analytics were analyzed. The study used selected open-source analytical tools, including Google Keyword Planner and Data Studio. The controversial hypothesis, which assumes that potential demand for wood and wood-based products shows a sustainable trend even under economic uncertainty, was verified. The findings were visualized and discussed. As a result, although the rationale for confirming the assumed hypothesis was not obtained. However, the results of the research proved to be interesting, out of the box. It turned out that the popularity and multifaceted use of wood is not limited by typical barriers. The consequence of the market situation is a growing demand for wood, but at the same time a supply deficit. This is a challenge for the wood market benchmarking and formulating practical recommendations for economic policy in the wood-based sector in Poland.

Predicting Actions of Users Using Heterogeneous Online Signals.

Advertising platforms have a growing need for improving prediction quality, as missing out on ad opportunities can have a negative effect on their performance. To that end, prediction tasks such as conversion prediction need to be continuously advanced through the inclusion of data from new sources or through algorithmic development that tackles existing challenges. The introduction of different data sources naturally brings unwanted noise, whereas underexplored areas still exist in modeling approaches, such as temporal information of events in sequences. In this study, we propose extensions for modeling online user activity trails that address two very important aspects of activities-time and noise, through dedicated layers that can be used in existing deep sequence-learning approaches. Our proposed method exhibited area under the receiver operating characteristic curve improvement of up to 3% and 1.75% compared with production and best baseline approaches, respectively, across two major advertiser data sets and several predictive tasks.

Live-Streamed Video Reconstruction for Web Browser Forensics

The way we use video streaming is evolving. Users used to broadcast their videos on social media platforms. These platforms enable them to interact from anywhere they want. Recently, there has been a wide range of people who use live video streaming platforms regularly. Thanks to high-speed Internet connections, live video streaming is now easier than ever. Many of these platforms broadcast live video feeds of electronic games, so young streamers use them to make money. Live streaming refers to media that is simultaneously broadcasted and recorded online in real-time. Despite the growing popularity of these platforms, there is a risk that this technology will be abused. Several other recorded cases of abuse have resulted in the emerging popularity of live streaming platforms. Many criminal and public proceedings may rely on information linked to a normal Web user's Online activity. Examining the web browser's history or cache may reveal helpful information about the suspect's activities. The evidence can reveal keys that might lead to this individual being convicted or clear. This work continues what was previously done to reconstruct cached video streams from YouTube and Twitter on Firefox. Our main aim in this paper is to examine data from a cached live stream using YouTube Gaming/Live and Nimo TV on Firefox and Chromium browsers.

Clients and professionals elicit long-term care preferences by using 'What matters to me': A process evaluation in the Netherlands.

Background‘What matters to me’ is a five‐category preference elicitation tool to assist clients and professionals in choosing long‐term care. This study aimed to evaluate the use of and experiences with this tool.MethodsA mixed‐method process evaluation was applied. Participants were 71 clients or relatives, and 12 professionals. They were all involved in decision‐making on long‐term care. Data collection comprised online user activity logs (N = 71), questionnaires (N = 38) and interviews (N = 20). Descriptive statistics was used for quantitative data, and a thematic analysis for qualitative data.ResultsSixty‐nine per cent of participants completed one or more categories in an average time of 6.9 (±0.03) minutes. The tool was rated 6.63 (±0.88) of 7 in the Post‐Study System Usability Questionnaire (PSSUQ). Ninety‐five per cent experienced the tool as useful in practice. Suggestions for improvement included a separate version for relatives and a non‐digital version. Although professionals thought the potentially extended consultation time could be problematic, all participants would recommend the tool to others.Conclusion‘What matters to me’ seems useful to assist clients and professionals with preference elicitation in long‐term care. Evaluation of the impact on consultations between clients and professionals by using ‘What matters to me’ is needed.

Assessment of quality of life in regions of Russia based on social media data

The article offers a new method of quality of life assessment based on online activities of social networks users. The method has obvious advantages (quickness of research, low costs, large scale, and detailed character of the obtained information) and limitations (it covers only the “digital population,” whereas the rural population is not included). The article dwells on the potential of social networks as a data source to analyze the quality of life; it also presents the results of an empirical study of online activities of the users of VK, the most popular Russian social network. Using the obtained data, the authors have calculated the quality of life index for 83 regions of the Russian Federation based on 19 parameters of economic, social, and political aspects of life quality.

Digital methods of analysis of subjective quality of life: case of Russian Regions

The paper presents a method for measuring subjective quality of life in the regions of the Russian Federation on the basis of digital data. Information about online activity of users in the largest social media in Russia - VKontakte was taken as the data source. Quality of Life Index was calculated based on the obtained data. The results show that overall users tend to negatively assess the quality of life in their regions, with the highest estimates of the quality of life observed in the “ethnic” areas of the Russian Federation – republics, autonomous territories, districts and regions, first of all, in North Caucuses, Siberia and the Russian Far East. The lowest quality-of-life assessments of are noted in some regions in West Siberia. The paper analyses how the results of measuring quality of life with digital methods correlate with objective social-and-economic and demographic indicators of regional development. Several regularities for the “ethnic” areas are revealed while in other areas (regions, territories, cities of federal status) no significant correlations were established. The paper also gives a comparative analysis of quality of life assessments obtained through traditional questionnaire methods and digital methods.

Online Tracking: When Does it Become Stalking?

Online user activities are tracked for many purposes. In e-commerce, cross-domain tracking is used to quantify and pay for web-traffic generation. Our previous research studies have shown that HTTP cookie-based tracking process, though reliable, can fail due to technical reasons, as well as through fraudulent manipulation by traffic generators. In this research study, we evaluate which of the previously published tracking mechanisms are still functional. We assess the efficacy and utility of those methods to create a robust tracking mechanism for e-commerce. A failsafe and robust tracking mechanism does not need to translate into further privacy intrusions. Many countries are rushing to introduce new regulations, which can have a negative impact on the development of robust technologies in an inherently stateless eco-system. We used a multi-domain, purpose-built simulation environment to experiment common tracking scenarios, and to describe the parameters that define the minimum tracking requirement use-cases, and practices that result in invading privacy of users. This study will help practitioners in their implementations, and policy developers and regulators to draw up policies that would not curtail the development of robust tracking technologies that are needed in e-commerce activities, while safeguarding the privacy of internet users.

A Study on Consent of the GDPR in Advertising Technology Focusing on Programmatic Buying

Online targeted advertising which conveys its advertising message to a target individual based on an accumulated data analysis across multiple external websites about a user's online activity. Basically, the targeted advertising is based on programmatic buying. In the past, advertisers could use a limited advertising space to attract potential customers such as a full-page advertisement at an influential daily or out-of-home(OOH) in the downtown. The advertisers had to win an auction, e.g. a private auction at offline venue, in order to get the space. Nowadays, however, as increasing online media’s influence, e.g. web portal, influencers’ blog, demand for the online advertising inventory is increasing faster than ever. The inventory can be supplied into the advertising market, because it has a feature as intangible resource. Under this circumstance, a method of the traditional action between advertisers and publishers became no longer working in the era of digital media. Hence, programmatic buying was introduced, because it provides that connect between advertisers’ demand and publishers’ supply automatically through an algorithm has already been set up. Moreover, the algorithm can support that advertisers the ability to reach potential customers. Using a personal data helps to select the potential customers. The personal data has been collected on the basis of individuals’ consent. The targeted advertisement is good for potential customers, giving them better access to information about what is being offered, however, it also has privacy risk to users. Therefore consent for marketing purposes become increasingly important in due course. Article 4(11) of the General Data Protection Regulation (GDPR), which was adopted on 14 April 2016, and became enforceable beginning 25 May 2018, states that “any freely given, specific, informed and unambiguous indication of the data subject's wishes by which he or she, by a statement or by a clear affirmative action, signifies agreement to the processing of personal data relating to him or her”. Usually, to use a web portal, the potential customers as user has to consent for the personal data to be used for marketing purposes, at the same time, the web poral as online media, known as publishers in programmatic buying, has a role to supply(or sell) advertising inventories for advertisers. Supply Side Platforms(SSPs) help publishers manage and sell the advertising inventories. Advertising exchanges(Ad exchanges) as mediators, connects between SSPs and Demand Side Platforms(DSPs), which is in charge of managing advertisers’ demand to buy the inventories. Advertisers conduct to bid in real-time to serve advertising impressions to webpage visitors. The highest bidder ‘wins’, and their advertisement will be presented on the webpage to the potential customer via the Real-Time Bidding(RTB) process. The whole process simultaneously takes place in milliseconds based on the authorism in embedded platform. Furthermore, the participants use multiple DSPs and/or SSPs to enable more accurate targeting of advertisements to individuals, i.e. potential customers, and to gain higher revenue, hence, it is hard to figure out that each of participants comply with Article 4(11) of the GDPR, step by step. Although the Article 29 Working Party(WP29) provides ‘Guidelines on consent under Regulation 2016/679’(Guidelines) to help the European Union Member States comply with it, and interpret a notion of consent in GDPR. However, in practice, it is hard to be applied in all cases. In this article, the author will analyze discuss the issue relating to consent of the GDPR in the programmatic ecosystem in accordance with WP29’s Guidelines. Hence the author will suggest participants, data protection authorities, and lawmakers to comply with Article 4(11) of the GDPR in practice, considering a feature of the RTB process.

Intelligent phishing detection scheme using deep learning algorithms

PurposePhishing attacks have evolved in recent years due to high-tech-enabled economic growth worldwide. The rise in all types of fraud loss in 2019 has been attributed to the increase in deception scams and impersonation, as well as to sophisticated online attacks such as phishing. The global impact of phishing attacks will continue to intensify, and thus, a more efficient phishing detection method is required to protect online user activities. To address this need, this study focussed on the design and development of a deep learning-based phishing detection solution that leveraged the universal resource locator and website content such as images, text and frames.Design/methodology/approachDeep learning techniques are efficient for natural language and image classification. In this study, the convolutional neural network (CNN) and the long short-term memory (LSTM) algorithm were used to build a hybrid classification model named the intelligent phishing detection system (IPDS). To build the proposed model, the CNN and LSTM classifier were trained by using 1m universal resource locators and over 10,000 images. Then, the sensitivity of the proposed model was determined by considering various factors such as the type of feature, number of misclassifications and split issues.FindingsAn extensive experimental analysis was conducted to evaluate and compare the effectiveness of the IPDS in detecting phishing web pages and phishing attacks when applied to large data sets. The results showed that the model achieved an accuracy rate of 93.28% and an average detection time of 25 s.Originality/valueThe hybrid approach using deep learning algorithm of both the CNN and LSTM methods was used in this research work. On the one hand, the combination of both CNN and LSTM was used to resolve the problem of a large data set and higher classifier prediction performance. Hence, combining the two methods leads to a better result with less training time for LSTM and CNN architecture, while using the image, frame and text features as a hybrid for our model detection. The hybrid features and IPDS classifier for phishing detection were the novelty of this study to the best of the authors' knowledge.

Enhanced Performance and Privacy for TLS over TCP Fast Open

Abstract Small TCP flows make up the majority of web flows. For them, the TCP three-way handshake induces significant delay overhead. The TCP Fast Open (TFO) protocol can significantly decrease this delay via zero round-trip time (0-RTT) handshakes for all TCP handshakes that follow a full initial handshake to the same host. However, this comes at the cost of privacy limitations and also has some performance limitations. In this paper, we investigate the TFP deployment on popular websites and browsers. We found that a client revisiting a web site for the first time fails to use an abbreviated TFO handshake in 40% of all cases due to web server load-balancing using multiple IP addresses. Our analysis further reveals significant privacy problems of the protocol design and implementation. Network-based attackers and online trackers can exploit TFO to track the online activities of users. As a countermeasure, we introduce a novel protocol called TCP Fast Open Privacy (FOP). TCP FOP prevents tracking by network attackers and impedes third-party tracking, while still allowing 0-RTT handshakes as in TFO. As a proof-of-concept, we have implemented the proposed protocol for the Linux kernel and a TLS library. Our measurements indicate that TCP FOP outperforms TLS over TFO when websites are served from multiple IP addresses.

Internet privacy, technology, and personal information

Computer programs are used to obtain and store information about the online activities of users of the web. Many people are concerned about this practice because they believe that it can violate users' rights to privacy or result in violations of them. This belief is based on the assumption that the information obtained and stored with the use of the programs includes personal information. My main aim in this paper is to argue that this assumption is false. I discuss the import of this result for computer ethics. If my thesis is correct, using the programs cannot violate a person's right to privacy and cannot result in violations of a right to privacy. Nevertheless it is prima facie morally wrong because, as I explain, it encourages people to perform actions that diminish the well-being of others. I discuss the implications of my thesis for the obligations of online businesses with regard to the relevant information.

Influencing web customers. Deep learning practical application

Abstract With the global expansion of internet businesses, companies look for possibilities to increase their turnovers in virtual media. In these circumstances, it is significant for companies to employ online user behavior data in order to understand user intents and, to some extent, influence user online activity. This research examines the practical solution of the stated goal through an application of deep learning models. The contribution of the current study is an investigation of the success of real-time influence on web portal users to conduct specific activities leading to the achievement of predefined business goals. The study investigates several top-rated over-sampling techniques while solving class imbalance problem to evaluate their impact on the deep learning model performance. The study was conducted on the Lithuanian online educational platform. The obtained results indicate the feasibility of the suggested method.

Using www-server statistical data for calculating altmetrics

The authors discuss the use of files being accumulated by library www-serves (log-files) for calculating altmetrics. The log-files comprise information on all queries therefore they can be used for calculating statistical indicators of online user activity index in compliance with the federal standard GOST 7.0.20-2014. The authors also analyze the provisions of the standard as referred to online user services control. The COUNTER format is the international format assigning the list and form for presenting statistical data on using digital resources. The current version 5, the COUNTER Code and the algorithm of master data (log-files) processing are discussed. The authors conclude that Russian libraries have not introduced the unified method for counting the requests to their digital resources so far. Meaningful combination of GOST requirements, COUNTER fundamental indicators and calculation of indicators obtained through log-files analysis enable to analyze the relevance of library digital resources to various user categories.

Personalized Context-aware Collaborative Online Activity Prediction

With the rapid development of Internet services and mobile devices, nowadays, users can connect to online services anytime and anywhere. Naturally, user's online activity behavior is coupled with time and location contexts and highly influenced by them. Therefore, personalized context-aware online activity modelling and prediction is very meaningful and necessary but also very challenging, due to the complicated relationship between users, activities, spatial and temporal contexts and data sparsity issues. To tackle the challenges, we introduce offline check-in data as auxiliary data and build a user-location-time-activity 4D-tensor and a location-time-POI 3D-tensor, aiming to model the relationship between different entities and transfer semantic features of time and location contexts among them. Accordingly, in this paper we propose a transfer learning based collaborative tensor factorization method to achieve personalized context-aware online activity prediction. Based on real-world datasets, we compare the performance of our method with several state-of-the-arts and demonstrate that our method can provide more effective prediction results in the high sparsity scenario. With only 30% of observed time and location contexts, our solution can achieve 40% improvement in predicting user's Top5 activity behavior in new time and location scenarios. Our study is the first step forward for transferring knowledge learned from offline check-in behavior to online activity prediction to provide better personalized context-aware recommendation services for mobile users.

Субъективная оценка (не)благополучия населения регионов РФ на основе данных социальных сетей

В статье обосновывается новый метод субъективной оценки благополучия, основанный на анализе онлайн-активности пользователей в социальных сетях. Предлагаемый метод обладает как преимуществами (быстрота исследования, небольшие затраты, масштабность, детальность полученной информации), так и определенными ограничениями (охват «цифрового населения», технические сложности при исследовании густонаселенных мегаполисов и т.д.). В статье представлены результаты эмпирического исследования онлайн-активности пользователей социальной сети «ВКонтакте». На основе полученных данных рассчитан индекс субъективного (не)благополучия для 43 регионов РФ по 19 показателям, охватывающих экономические, социальные и политические аспекты качества жизни людей. Индекс субъективного (не)благополучия строится на изучении онлайн-активности пользователей, входящих в 1350 наиболее популярных региональных и городских сообществ в социальной сети «ВКонтакте». Период исследования включает в себя весь 2018 год.
 Благодарность. Исследование выполнено при финансовой поддержке «Программы повышения конкурентоспособности Томского государственного университета» (грант № 8.1.59.2018).

Sharing contextual knowledge information via asynchronous distance learning

This paper expands on the effective implementation of collaboration platforms for research purposes in primary education settings. In our study, Edmodo has been introduced as a medium for facilitating the asynchronous discourse between learners of Guadeloupe and Quebec. The following analysis is based on the digital traces derived from the online activity of users working on two different disciplinary research projects: one in linguistics and one in education for sustainable development (ESD). In essence, this paper covers the procedure of introducing a collaborative tool for educational purposes to an audience with diverse expertise in understanding and using it. In addition, it provides a conceptual analysis for understanding the online messages exchanged during these context-related interactions.

Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media

To what extent user's stance towards a given topic could be inferred? Most of the studies on stance detection have focused on analysing user's posts on a given topic to predict the stance. However, the stance in social media can be inferred from a mixture of signals that might reflect user's beliefs including posts and online interactions. This paper examines various online features of users to detect their stance towards different topics. We compare multiple set of features, including on-topic content, network interactions, user's preferences, and online network connections. Our objective is to understand the online signals that can reveal the users' stance. Experimentation is applied on tweets dataset from the SemEval stance detection task, which covers five topics. Results show that stance of a user can be detected with multiple signals of user's online activity, including their posts on the topic, the network they interact with or follow, the websites they visit, and the content they like. The performance of the stance modelling using different network features are comparable with the state-of-the-art reported model that used textual content only. In addition, combining network and content features leads to the highest reported performance to date on the SemEval dataset with F-measure of 72.49%. We further present an extensive analysis to show how these different set of features can reveal stance. Our findings have distinct privacy implications, where they highlight that stance is strongly embedded in user's online social network that, in principle, individuals can be profiled from their interactions and connections even when they do not post about the topic.

A Neural Network-Inspired Approach for Improved and True Movie Recommendations.

In the last decade, sentiment analysis, opinion mining, and subjectivity of microblogs in social media have attracted a great deal of attention of researchers. Movie recommendation systems are the tools, which provide valuable services to the users. The data available online are growing gradually because the online activities of users or viewers are increasing day by day. Because of this, big data, analytics, and computational issues have raised. Therefore, we have to improve recommendations services upon the traditional one to make the recommendation system significant and efficient. This article presents the solution for these issues by producing the significant and efficient recommendation services using multivariates (ratings, votes, Twitter likes, and reviews) of movies from multiple external resources which are fetched by the web bot and managed by the Apache Hadoop framework in a distributed manner. Reviews are analyzed by a deep semantic analyzer based on the recurrent neural network (RNN/LSTM attention) with user movie attention (UMA) to produce the emotion. The proposed recommender evaluates multivariates and produces a more significant movie recommendation list according to the taste of the user on a mobile app in an efficient way.