Es scheint in den letzten Jahren eine deutliche Veränderung in der Art der Kommunikation mit den sogenannten Neuen Kommunikationstechnologien stattgefunden zu haben, und zwar im Längsschnitt, also etwa über die letzten 10, 15 Jahre, wie auch zwischen den sich aus heutiger Sicht herausentwickelt habenden Nutzergruppen. Quantitative Erhebungen, am ausgeprägtesten die regelmäßig unternommenen und von den Medien wiedergegebenen "Internetumfragen", ebnen solche Veränderungen naturgemäß vollständig ein. Auch die vorhandenen qualitativen Arbeiten reflektieren solche Veränderungen meist nicht, da zeitliche Dimensionen üblicherweise in den Fragestellungen fehlen. Der Beitrag versucht eine Skizze dieser Entwicklungen, möchte einige Mißverständnisse im Zusammenhang mit neuer "Elektronischer Kommunikation" korrigieren helfen und kommunikationsökonomische Aspekte beisteuern.

In typical restaurant recommendations, knowledge-based methods are used most often and do not take advantage of personal historical data. In this thesis, we are going to make some improvements to the Chicago Entrée restaurant recommender system. We will exploit the historical data and propose a weighted similarity approach to combine heuristic similarity with tag similarity between restaurants. Also, we show an improved way to mine the semantics of user behaviors using heuristic metric. These proposed approaches are evaluated by the comparison of three different pairwise approaches to learning to rank (LTR) in matrix factorization and five classic recommendation algorithms. The result shows that the combinatorial similarity outperforms the heuristic similarity on the precision, recall, F-score, and mean reciprocal rank.

This thesis investigates how quantitative user data, extracted from server logs, and clustering algorithms can be used to model and understand user-behavior. The thesis also investigates how the results compare to the more traditional method of qualitative user-behavior analysis through interviews and observations. The results show that clustering of all user data, as opposed to interviewing only a small subset of users, increases the reliability of findings. However, the quantitative method has a risk of missing important insights that can only be discovered through observation of the user. The conclusion drawn in this thesis is that a combination of both is necessary to truly understand the user-behavior. / Denna uppsats undersöker hur kvantitativ användardata, extraherad från serverloggar, och klustringsalgoritmer kan användas för att modellera och förstå användarbeteende. Uppsatsen undersöker också hur resultatet av denna metod skiljer sig från resultatet av den mer traditionella kvalitativa metoden för användarbeteendeanalys, baserad på intervjuer och observationer. Resultatet visar att klustring av all användardata, istället för att intervjuer med endast en delmängd av användarna, ökar pålitligheten i analysen. Dock visar resultatet också att den kvantitativa metoden riskerar att missa viktiga insikter som bara kan upptäckas med hjälp av observationer. Slutsatsen är att en kombination av både den kvantitativa och den kvalitativa metoden behövs för att helt kunna förstå användarbeteendet.

Why do some employees go out of their way to follow prescribed information security protocols, while others all but ignore organizational information security measures? A body of research known as organizational citizenship behavior provides insight into this issue. Theories of organizational citizenship behavior draw mainly from the psychological and sociological disciplines. They are used to explain the behaviors of employees who act in the best interest of the company, even when they don’t have to. Examples of citizenship behaviors include information sharing, voluntary reduction of compensation, and relinquishment of power for the benefit of the organization (Nathanson & Becker 1973). Although organizational citizenship behavior has seen little exposure in the area of organizational information security compliance, it stands to provide exceptional explanatory power in this area. Information security practices, such as creating difficult passwords or conducting virus scans, are generally seen as additional tasks which require extra effort while offering no gains in personal productivity (Shropshire et al., 2006; Warkentin et al., 2004; Warkentin et al., 2006). These activities could be construed as out-of-role-behaviors because employee compliance may not be mandatory. Furthermore, it is difficult to enforce information security standards (Whitman, 2003). Thus, it would appear that those who follow information security protocols are motivated by something other than financial compensation. Currently, there has been little work toward integrating endpoint security with theories of organizational citizenship behavior. This may be due to two reasons: although it embodies a relatively mature stream of research, organizational citizenship behavior has seen little exposure within the information systems context; secondly, the behavioral aspects of endpoint security remain a critical but overlooked aspect of organizational information security. Therefore, the purpose of this research is to develop a theoretical model for predicting individual compliance with organizational information security practices. The results could be used by managers to more accurately predict adherence to information security practices and to better manage and motivate employees. Such a model might also be of utility in the area of employee selection and screening; recent political and economic events have caused an increase in demand for employees who can be trusted to safeguard sensitive information. This study provides a substantial contribution to knowledge by empirically testing a predictive model for information security compliance among employees. The findings associated with this research are offered in the form of recommendations for future theoretical and empirical research. Practical implications for entrepreneurs and policymakers are also discussed.

The purpose of this interdisciplinary study was to explore whether it is possible to conduct a data-driven study using pattern recognition in order to gain an understanding of user behavior within a mobile bank application. This knowledge was in turn used to propose ways of tailoring the application to better suit the actual needs of the users. In this thesis, unsupervised learning in the form of clustering was applied to a data set containing information about user interactions with a mobile bank application. By pre-processing the data, finding the best value for the number of clusters to use and applying these results to the K-means algorithm, clustering into distinct subgroups was possible. Visualization of the clusters was possible due to combining K-means with a Principal Component Analysis. Through clustering, patterns regarding how the different functionalities are used in the application were revealed. Thereafter, using relevant concepts within the field of human-computer interaction, a proposal was made of how the application could be altered to better suit the discovered needs of the users. The results show that most sessions are passive, that the device model is of high importance in the clusters, that some features are seldom used and that hidden functionalities are not used in full measure. This is either due to the user not wanting to use some functionalities or because there is a lack of discoverability or understanding among the users, causing them to refrain from using these functionalities. However, determining the actual cause requires further qualitative studies. Removing features which are seldom used, adding signifiers, active discovery as well as conducting user-tests are identified as possible actions in order to minimize issues with discoverability and understanding. Finally, future work and possible improvements to the research methods used in this study were proposed.

Online communities provide a fertile ground for analyzing people\'s behavior and improving our understanding of social processes. For instance, when modeling social interaction online, it is important to understand when people are reacting to each other. Also, since both people and communities change over time, we argue that analyses of online communities that take time into account will lead to deeper and more accurate results. In many cases, however, users behavior can be easily missed: users react to content in many more ways than observed by explicit indicators (such as likes on Facebook or replies on Twitter) and poorly aggregated temporal data might hide, misrepresent and even lead to wrong conclusions about how users are evolving. In order to address the problem of detecting non-explicit responses, we present a new approach that uses tf-idf similarity between a user\'s own tweets and recent tweets by people they follow. Based on a month\'s worth of posting data from 449 ego networks in Twitter, this method demonstrates that it is likely that at least 11% of reactions are not captured by the explicit reply and retweet mechanisms. Further, these uncaptured reactions are not evenly distributed between users: some users, who create replies and retweets without using the official interface mechanisms, are much more responsive to followees than they appear. This suggests that detecting non-explicit responses is an important consideration in mitigating biases and building more accurate models when using these markers to study social interaction and information diffusion. We also address the problem of users evolution in Reddit based on comment and submission data from 2007 to 2014. Even using one of the simplest temporal differences between usersyearly cohortswe find wide differences in people\'s behavior, including comment activity, effort, and survival. Furthermore, not accounting for time can lead us to misinterpret important phenomena. For instance, we observe that average comment length decreases over any fixed period of time, but comment length in each cohort of users steadily increases during the same period after an abrupt initial drop, an example of Simpson\'s Paradox. Dividing cohorts into sub-cohorts based on the survival time in the community provides further insights; in particular, longer-lived users start at a higher activity level and make more and shorter comments than those who leave earlier. These findings both give more insight into user evolution in Reddit in particular, and raise a number of interesting questions around studying online behavior going forward. / Comunidades online proporcionam um ambiente fértil para análise do comportamento de indivíduos e processos sociais. Por exemplo, ao modelarmos interações sociais online, é importante compreendemos quando indivíduos estão reagindo a outros indivíduos. Além disso, pessoas e comunidades mudam com o passar do tempo, e levar em consideração sua evolução temporal nos leva a resultados mais precisos. Entretanto, em muitos casos, o comportamento dos usuários pode ser perdido: suas reações ao conteúdo ao qual são expostos não são capturadas por indicadores explícitos (likes no Facebook, replies no Twitter). Agregações temporais de dados pouco criteriosas podem ocultar, enviesar ou até levar a conclusões equivocadas sobre como usuários evoluem. Apresentamos uma nova abordagem para o problema de detectar respostas não-explicitas que utiliza similaridade tf-idf entre tweets de um usuário e tweets recentes que este usuário recebeu de quem segue. Com base em dados de postagens de um mês para 449 redes egocêntricas do Twitter, este método evidencia que temos um volume de ao menos 11% de reações não capturadas pelos mecanismos explicitos de reply e retweet. Além disso, essas reações não capturadas não estão uniformemente distribuídas entre os usuários: alguns usuários que criam replies e retweets sem utilizar os mecanismos formais da interface são muito mais responsivos a quem eles seguem do que aparentam. Isso sugere que detectar respostas não-explicitas é importante para mitigar viéses e construir modelos mais precisos a fim de estudar interações sociais e difusão de informação. Abordamos o problema de evolução de usuários no Reddit com base em dados entre o período de 2007 a 2014. Utilizando métodos simples de diferenciação temporal dos usuários -- cohorts anuais -- encontramos amplas diferenças entre o comportamento, que incluem criação de comentários, métricas de esforço e sobrevivência. Desconsiderar a evolução temporal pode levar a equívocos a respeito de fenômenos importantes. Por exemplo, o tamanho médio dos comentários na rede decresce ao longo de qualquer intervalo de tempo, mas este tamanho é crescente em cada uma das cohorts de usuários no mesmo período, salvo de uma queda inicial. Esta é uma observação do Paradoxo de Simpson. Dividir as cohorts de usuários em sub-cohorts baseadas em anos de sobrevivência na rede nos fornece uma perspectiva melhor; usuários que sobrevivem por mais tempo apresentam um maior nível de atividade inicial, com comentários mais curtos do que aqueles que sobrevivem menos. Com isto, compreendemos melhor como usuários evoluem no Reddit e levantamos uma série de questões a respeito de futuros desdobramentos do estudo de comportamento online.

Jali screens are traditional window treatments in vernacular buildings throughout South Asia and the Middle East. Contemporary builders are starting to incorporate Jali screens as decorative façade elements; however, architects and scholars have largely ignored the impact of Jali screens on overall building energy and day-lighting performance. This research evaluates the effect of Jali screens, across a range of perforation ratios, on energy utilization and day-lighting quality in contemporary office buildings. The data collection and analysis is through fieldwork in Lahore, Pakistan, as well as through computational energy modeling. Results demonstrate that Jali screens have a promising positive impact on cooling loads and may improve visual comfort. The findings suggest a holistic perspective combining traditional architecture and performance enhancement by architects and designers.

abstract: Due to the growing popularity of the Internet and smart mobile devices, massive data has been produced every day, particularly, more and more users’ online behavior and activities have been digitalized. Making a better usage of the massive data and a better understanding of the user behavior become at the very heart of industrial firms as well as the academia. However, due to the large size and unstructured format of user behavioral data, as well as the heterogeneous nature of individuals, it leveled up the difficulty to identify the SPECIFIC behavior that researchers are looking at, HOW to distinguish, and WHAT is resulting from the behavior. The difference in user behavior comes from different causes; in my dissertation, I am studying three circumstances of behavior that potentially bring in turbulent or detrimental effects, from precursory culture to preparatory strategy and delusory fraudulence. Meanwhile, I have access to the versatile toolkit of analysis: econometrics, quasi-experiment, together with machine learning techniques such as text mining, sentiment analysis, and predictive analytics etc. This study creatively leverages the power of the combined methodologies, and apply it beyond individual level data and network data. This dissertation makes a first step to discover user behavior in the newly boosting contexts. My study conceptualize theoretically and test empirically the effect of cultural values on rating and I find that an individualist cultural background are more likely to lead to deviation and more expression in review behaviors. I also find evidence of strategic behavior that users tend to leverage the reporting to increase the likelihood to maximize the benefits. Moreover, it proposes the features that moderate the preparation behavior. Finally, it introduces a unified and scalable framework for delusory behavior detection that meets the current needs to fully utilize multiple data sources. / Dissertation/Thesis / Doctoral Dissertation Business Administration 2019

Understanding Internet user behavior and Internet usage patterns is fundamental in developing future access networks and services that meet technical as well as Internet user needs. User behavior is routinely studied and measured, but with different methods depending on the research discipline of the investigator, and these disciplines rarely cross. We tackle this challenge by developing frameworks that the Internet usage statistics used as the main features in understanding Internet user behaviors, with the purpose of finding a complete picture of the user behavior and working towards a unified analysis methodology. In this dissertation we collected Internet usage statistics via privacy-preserving NetFlow logs of 66 student subjects in a college campus was recorded for a month long period. Once the data is cleaned and split into different groups based on different time windows, we have used Statistical Analysis and we found that Internet usage of each user exhibits statistically-strong correlation with the same user's Internet usage for the same day over multiple weeks while it is statistically different from that of other Internet users. In another attempt we have used Time Series Forecasting in order to forecast future Internet usage based on the previous statistics. Subsequently, using state-of-the-art Machine Learning algorithms, we demonstrate the feasibility of profiling Internet users by looking at their Internet traffic. Specifically, when profiled over a time window of 227-second, subjects can be classified by 93.21% precision accuracy. We conclude that understanding Internet usage behavior is valuable and can help in developing future access networks and services.

Interpreting user actions to better understand their needs provides an important tool for improving information access services. In the context of organic Web search, considerable effort has been made to model user behavior and infer query intent, with the goal of improving the overall user experience. Much less work has been done in the area of sponsored search, i.e., with respect to the advertisement links (ads) displayed on search result pages by many commercial search engines. This thesis develops and evaluates new models and methods required to interpret user browsing and click behavior and understand query intent in this very different context. The concern of the initial part of the thesis is on extending the query categories for commercial search and on inferring query intent, with a focus on two major tasks: i) enriching queries with contextual information obtained from search result pages returned for these queries, and ii) developing relatively simple methods for the reliable labeling of training data via crowdsourcing. A central idea of this thesis work is to study the impact of contextual factors (including query intent, ad placement, and page structure) on user behavior. Later, this information is incorporated into probabilistic models to evaluate the quality of advertisement links within the context that they are displayed in their history of appearance. In order to account for these factors, a number of query and location biases are proposed and formulated into a group of browsing and click models. To explore user intent and behavior and to evaluate the performance of the proposed models and methods, logs of query and click information provided for research purposes are used. Overall, query intent is found to have substantial impact on predictions of user click behavior in sponsored search. Predictions are further improved by considering ads in the context of the other ads displayed on a result page. The parameters of the browsing and click models are learned using an expectation maximization technique applied to click signals recorded in the logs. The initial motivation of the user to browse the ad list and their browsing persistence are found to be related to query intent and browsing/click behavior. Accommodating these biases along with the location bias in user models appear as effective contextual signals, improving the performance of the existing models.