Philipp Singer

Detecting Memory and Structure in Human Navigation Patterns Using Markov Chain Models of Varying Order

One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Googles PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.

Evolution of reddit: from the front page of the internet to a self-referential community?

In the past few years, Reddit -- a community-driven platform for submitting, commenting and rating links and text posts -- has grown exponentially, from a small community of users into one of the largest online communities on the Web. To the best of our knowledge, this work represents the most comprehensive longitudinal study of Reddits evolution to date, studying both (i) how user submissions have evolved over time and (ii) how the communitys allocation of attention and its perception of submissions have changed over 5 years based on an analysis of almost 60 million submissions. Our work reveals an ever-increasing diversification of topics accompanied by a simultaneous concentration towards a few selected domains both in terms of posted submissions as well as perception and attention. By and large, our investigations suggest that Reddit has transformed itself from a dedicated gateway to the Web to an increasingly self-referential community that focuses on and reinforces its own user-generated image- and textual content over external sources.

HypTrails: A Bayesian Approach for Comparing Hypotheses About Human Trails on the Web

When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.

Semantic stability in social tagging streams

One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, users or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources may become semantically stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the semantic stability of social tagging systems in a robust and methodical way? (ii) Does semantic stabilization of tags vary across different social tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by (i) presenting a novel and robust method which overcomes a number of limitations in existing methods, (ii) empirically investigating semantic stabilization processes in a wide range of social tagging systems with distinct domains and properties and (iii) detecting potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams which are generated via imitation dynamics or natural language phenomena alone.

Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains

Biomedical taxonomies, thesauri and ontologies in the form of the International Classification of Diseases as a taxonomy or the National Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in acquiring, representing and processing information about human health. With increasing adoption and relevance, biomedical ontologies have also significantly increased in size. For example, the 11th revision of the International Classification of Diseases, which is currently under active development by the World Health Organization contains nearly 50,000 classes representing a vast variety of different diseases and causes of death. This evolution in terms of size was accompanied by an evolution in the way ontologies are engineered. Because no single individual has the expertise to develop such large-scale ontologies, ontology-engineering projects have evolved from small-scale efforts involving just a few domain experts to large-scale projects that require effective collaboration between dozens or even hundreds of experts, practitioners and other stakeholders. Understanding the way these different stakeholders collaborate will enable us to improve editing environments that support such collaborations. In this paper, we uncover how large ontology-engineering projects, such as the International Classification of Diseases in its 11th revision, unfold by analyzing usage logs of five different biomedical ontology-engineering projects of varying sizes and scopes using Markov chains. We discover intriguing interaction patterns (e.g., which properties users frequently change after specific given ones) that suggest that large collaborative ontology-engineering projects are governed by a few general principles that determine and drive development. From our analysis, we identify commonalities and differences between different projects that have implications for project managers, ontology editors, developers and contributors working on collaborative ontology-engineering projects and tools in the biomedical domain.

Meaning as collective use: predicting semantic hashtag categories on twitter

This paper sets out to explore whether data about the usage of hashtags on Twitter contains information about their semantics. Towards that end, we perform initial statistical hypothesis tests to quantify the association between usage patterns and semantics of hashtags. To assess the utility of pragmatic features - which describe how a hashtag is used over time - for semantic analysis of hashtags, we conduct various hashtag stream classification experiments and compare their utility with the utility of lexical features. Our results indicate that pragmatic features indeed contain valuable information for classifying hashtags into semantic categories. Although pragmatic features do not outperform lexical features in our experiments, we argue that pragmatic features are important and relevant for settings in which textual information might be sparse or absent (e.g., in social video streams).

Discovering and Characterizing Mobility Patterns in Urban Spaces: A Study of Manhattan Taxi Data

Nowadays, human movement in urban spaces can be traced digitally in many cases. It can be observed that movement patterns are not constant, but vary across time and space. In this work, we characterize such spatio-temporal patterns with an innovative combination of two separate approaches that have been utilized for studying human mobility in the past. First, by using non-negative tensor factorization (NTF), we are able to cluster human behavior based on spatio-temporal dimensions. Second, for characterizing these clusters, we propose to use HypTrails, a Bayesian approach for expressing and comparing hypotheses about human trails. To formalize hypotheses, we utilize publicly available Web data (i.e., Foursquare and census data). By studying taxi data in Manhattan, we can discover and characterize human mobility patterns that cannot be identified in a collective analysis. As one example, we find a group of taxi rides that end at locations with a high number of party venues on weekend nights. Our findings argue for a more fine-grained analysis of human mobility in order to make informed decisions for e.g., enhancing urban structures, tailored traffic control and location-based recommender systems.

Photowalking the City: Comparing Hypotheses About Urban Photo Trails on Flickr

Understanding human movement trajectories represents an important problem that has implications for a range of societal challenges such as city planning and evolution, public transport or crime. In this paper, we focus on geo-temporal photo trails from four different cities (Berlin, London, Los Angeles, New York) derived from Flickr that are produced by humans when taking sequences of photos in urban areas. We apply a Bayesian approach called HypTrails to assess different explanations of how the trails are produced. Our results suggest that there are common processes underlying the photo trails observed across the studied cities. Furthermore, information extracted from social media, in the form of concepts and usage statistics from Wikipedia, allows for constructing explanations for human movement trajectories.

Evidence of Online Performance Deterioration in User Sessions on Reddit.

This article presents evidence of performance deterioration in online user sessions quantified by studying a massive dataset containing over 55 million comments posted on Reddit in April 2015. After segmenting the sessions (i.e., periods of activity without a prolonged break) depending on their intensity (i.e., how many posts users produced during sessions), we observe a general decrease in the quality of comments produced by users over the course of sessions. We propose mixed-effects models that capture the impact of session intensity on comments, including their length, quality, and the responses they generate from the community. Our findings suggest performance deterioration: Sessions of increasing intensity are associated with the production of shorter, progressively less complex comments, which receive declining quality scores (as rated by other users), and are less and less engaging (i.e., they attract fewer responses). Our contribution evokes a connection between cognitive and attention dynamics and the usage of online social peer production platforms, specifically the effects of deterioration of user performance.

Spatial and temporal patterns of online food preferences

Since food is one of the central elements of all human beings, a high interest exists in exploring temporal and spatial food and dietary patterns of humans. Predominantly, data for such investigations stem from consumer panels which continuously capture food consumption patterns from individuals and households. In this work we leverage data from a large online recipe platform which is frequently used in the German speaking regions in Europe and explore (i) the association between geographic proximity and shared food preferences and (ii) to what extent temporal information helps to predict the food preferences of users. Our results reveal that online food preferences of geographically closer regions are more similar than those of distant ones and show that specific types of ingredients are more popular on specific days of the week. The observed patterns can successfully be mapped to known real-world patterns which suggests that existing methods for the investigation of dietary and food patterns (e.g., consumer panels) may benefit from incorporating the vast amount of data generated by users browsing recipes on the Web.