arXiv subjects

Florian Lemmerich

Publications and source records attributed to Florian Lemmerich.

36 records · Page 2Linked to original sources

arXiv2021

The FairCeptron: A Framework for Measuring Human Perceptions of Algorithmic Fairness

Measures of algorithmic fairness often do not account for human perceptions of fairness that can substantially vary between different sociodemographics and stakeholders. The FairCeptron framework is an approach for studying perceptions of fairness in algorithmic decision making such as in ranking or classification. It supports (i) studying human perceptions of fairness and (ii) comparing these human perceptions with measures of algorithmic fairness. The framework includes fairness scenario generation, fairness perception elicitation and fairness perception analysis. We demonstrate the FairCeptron framework by applying it to a hypothetical university admission context where we collect human perceptions of fairness in the presence of minorities. An implementation of the FairCeptron framework is openly available, and it can easily be adapted to study perceptions of algorithmic fairness in other application contexts. We hope our work paves the way towards elevating the role of studies of human fairness perceptions in the process of designing algorithmic decision making systems.

cs.CY↗

arXiv2020

From Asking to Answering: Getting More Involved on Stack Overflow

Online knowledge platforms such as Stack Overflow and Wikipedia rely on a large and diverse contributor community. Despite efforts to facilitate onboarding of new users, relatively few users become core contributors, suggesting the existence of barriers or hurdles that hinder full involvement in the community. This paper investigates such issues on Stack Overflow, a widely popular question and answer community for computer programming. We document evidence of a "leaky pipeline", specifically that there are many active users on the platform who never post an answer. Using this as a starting point, we investigate potential factors that can be linked to the transition of new contributors from asking questions to posting answers. We find a user's individual features, such as their tenure, gender, and geographic location, as well as features of the subcommunity in which they are most active, such as its size and the prevalence of negative social feedback, have a significant relationship with their likelihood to post answers. By measuring and modeling these relationships our paper presents a first look at the challenges and obstacles to user promotion along the pipeline of contributions in online communities.

cs.CY↗

arXiv2020

Global gender differences in Wikipedia readership

Wikipedia represents the largest and most popular source of encyclopedic knowledge in the world today, aiming to provide equal access to information worldwide. From a global online survey of 65,031 readers of Wikipedia and their corresponding reading logs, we present novel evidence of gender differences in Wikipedia readership and how they manifest in records of user behavior. More specifically we report that (1) women are underrepresented among readers of Wikipedia, (2) women view fewer pages per reading session than men do, (3) men and women visit Wikipedia for similar reasons, and (4) men and women exhibit specific topical preferences. Our findings lay the foundation for identifying pathways toward knowledge equity in the usage of online encyclopedic knowledge.

cs.CY↗

arXiv2020

Quota-based debiasing can decrease representation of already underrepresented groups

Many important decisions in societies such as school admissions, hiring, or elections are based on the selection of top-ranking individuals from a larger pool of candidates. This process is often subject to biases, which typically manifest as an under-representation of certain groups among the selected or accepted individuals. The most common approach to this issue is debiasing, for example via the introduction of quotas that ensure proportional representation of groups with respect to a certain, often binary attribute. Cases include quotas for women on corporate boards or ethnic quotas in elections. This, however, has the potential to induce changes in representation with respect to other attributes. For the case of two correlated binary attributes we show that quota-based debiasing based on a single attribute can worsen the representation of already underrepresented groups and decrease overall fairness of selection. We use several data sets from a broad range of domains from recidivism risk assessments to scientific citations to assess this effect in real-world settings. Our results demonstrate the importance of including all relevant attributes in debiasing procedures and that more efforts need to be put into eliminating the root causes of inequalities as purely numerical solutions such as quota-based debiasing might lead to unintended consequences.

cs.CY↗

arXiv2020

The Effects of Randomness on the Stability of Node Embeddings

We systematically evaluate the (in-)stability of state-of-the-art node embedding algorithms due to randomness, i.e., the random variation of their outcomes given identical algorithms and graphs. We apply five node embeddings algorithms---HOPE, LINE, node2vec, SDNE, and GraphSAGE---to synthetic and empirical graphs and assess their stability under randomness with respect to (i) the geometry of embedding spaces as well as (ii) their performance in downstream tasks. We find significant instabilities in the geometry of embedding spaces independent of the centrality of a node. In the evaluation of downstream tasks, we find that the accuracy of node classification seems to be unaffected by random seeding while the actual classification of nodes can vary significantly. This suggests that instability effects need to be taken into account when working with node embeddings. Our work is relevant for researchers and engineers interested in the effectiveness, reliability, and reproducibility of node embedding approaches.

cs.LG↗

arXiv2020

Joint Multiclass Debiasing of Word Embeddings

Bias in Word Embeddings has been a subject of recent interest, along with efforts for its reduction. Current approaches show promising progress towards debiasing single bias dimensions such as gender or race. In this paper, we present a joint multiclass debiasing approach that is capable of debiasing multiple bias dimensions simultaneously. In that direction, we present two approaches, HardWEAT and SoftWEAT, that aim to reduce biases by minimizing the scores of the Word Embeddings Association Test (WEAT). We demonstrate the viability of our methods by debiasing Word Embeddings on three classes of biases (religion, gender and race) in three different publicly available word embeddings and show that our concepts can both reduce or even completely eliminate bias, while maintaining meaningful relationships between vectors in word embeddings. Our work strengthens the foundation for more unbiased neural representations of textual data.

cs.CL↗

arXiv2020

The POLAR Framework: Polar Opposites Enable Interpretability of Pre-Trained Word Embeddings

We introduce POLAR - a framework that adds interpretability to pre-trained word embeddings via the adoption of semantic differentials. Semantic differentials are a psychometric construct for measuring the semantics of a word by analysing its position on a scale between two polar opposites (e.g., cold -- hot, soft -- hard). The core idea of our approach is to transform existing, pre-trained word embeddings via semantic differentials to a new "polar" space with interpretable dimensions defined by such polar opposites. Our framework also allows for selecting the most discriminative dimensions from a set of polar dimensions provided by an oracle, i.e., an external source. We demonstrate the effectiveness of our framework by deploying it to various downstream tasks, in which our interpretable word embeddings achieve a performance that is comparable to the original word embeddings. We also show that the interpretable dimensions selected by our framework align with human judgement. Together, these results demonstrate that interpretability can be added to word embeddings without compromising performance. Our work is relevant for researchers and engineers interested in interpreting pre-trained word embeddings.

cs.CL↗

arXiv2020

The Effects of Gender Signals and Performance in Online Product Reviews

This work quantifies the effects of signaling and performing gender on the success of reviews written on the popular amazon shopping platform. Highly rated reviews play an important role in e-commerce since they are prominently displayed below products. Differences in how gender-signaling and gender-performing review authors are received can lead to important biases in what content and perspectives are represented among top reviews. To investigate this, we extract signals of author gender from user names, distinguishing reviews where the author's likely gender can be inferred. Using reviews authored by these gender-signaling authors, we train a deep-learning classifier to quantify the gendered writing style or gendered performance of reviews written by authors who do not send clear gender signals via their user name. We contrast the effects of gender signaling and performance on review success using matching experiments. While we find no general trend that gendered signals or performances influence overall review success, we find strong context-specific effects. For example, reviews in product categories such as Electronics or Computers are perceived as less helpful when authors signal that they are likely woman, but are received as more helpful in categories such as Beauty or Clothing. In addition to these interesting findings, our work provides a general chain of tools for studying gender-specific effects across various social media platforms.

cs.CY↗

arXiv2019

Privacy Attacks on Network Embeddings

Data ownership and data protection are increasingly important topics with ethical and legal implications, e.g., with the right to erasure established in the European General Data Protection Regulation (GDPR). In this light, we investigate network embeddings, i.e., the representation of network nodes as low-dimensional vectors. We consider a typical social network scenario with nodes representing users and edges relationships between them. We assume that a network embedding of the nodes has been trained. After that, a user demands the removal of his data, requiring the full deletion of the corresponding network information, in particular the corresponding node and incident edges. In that setting, we analyze whether after the removal of the node from the network and the deletion of the vector representation of the respective node in the embedding significant information about the link structure of the removed node is still encoded in the embedding vectors of the remaining nodes. This would require a (potentially computationally expensive) retraining of the embedding. For that purpose, we deploy an attack that leverages information from the remaining network and embedding to recover information about the neighbors of the removed node. The attack is based on (i) measuring distance changes in network embeddings and (ii) a machine learning classifier that is trained on networks that are constructed by removing additional nodes. Our experiments demonstrate that substantial information about the edges of a removed node/user can be retrieved across many different datasets. This implies that to fully protect the privacy of users, node deletion requires complete retraining - or at least a significant modification - of original network embeddings. Our results suggest that deleting the corresponding vector representation from network embeddings alone is not sufficient from a privacy perspective.

cs.LG↗

arXiv2019

HopRank: How Semantic Structure Influences Teleportation in PageRank (A Case Study on BioPortal)

This paper introduces HopRank, an algorithm for modeling human navigation on semantic networks. HopRank leverages the assumption that users know or can see the whole structure of the network. Therefore, besides following links, they also follow nodes at certain distances (i.e., k-hop neighborhoods), and not at random as suggested by PageRank, which assumes only links are known or visible. We observe such preference towards k-hop neighborhoods on BioPortal, one of the leading repositories of biomedical ontologies on the Web. In general, users navigate within the vicinity of a concept. But they also "jump" to distant concepts less frequently. We fit our model on 11 ontologies using the transition matrix of clickstreams, and show that semantic structure can influence teleportation in PageRank. This suggests that users--to some extent--utilize knowledge about the underlying structure of ontologies, and leverage it to reach certain pieces of information. Our results help the development and improvement of user interfaces for ontology exploration.

cs.SI↗

arXiv2018

Why the World Reads Wikipedia: Beyond English Speakers

As one of the Web's primary multilingual knowledge sources, Wikipedia is read by millions of people across the globe every day. Despite this global readership, little is known about why users read Wikipedia's various language editions. To bridge this gap, we conduct a comparative study by combining a large-scale survey of Wikipedia readers across 14 language editions with a log-based analysis of user activity. We proceed in three steps. First, we analyze the survey results to compare the prevalence of Wikipedia use cases across languages, discovering commonalities, but also substantial differences, among Wikipedia languages with respect to their usage. Second, we match survey responses to the respondents' traces in Wikipedia's server logs to characterize behavioral patterns associated with specific use cases, finding that distinctive patterns consistently mark certain use cases across language editions. Third, we show that certain Wikipedia use cases are more common in countries with certain socio-economic characteristics; e.g., in-depth reading of Wikipedia articles is substantially more common in countries with a low Human Development Index. These findings advance our understanding of reader motivations and behaviors across Wikipedia languages and have implications for Wikipedia editors and developers of Wikipedia and other Web technologies.

cs.CY↗

arXiv2018

Query for Architecture, Click through Military: Comparing the Roles of Search and Navigation on Wikipedia

As one of the richest sources of encyclopedic information on the Web, Wikipedia generates an enormous amount of traffic. In this paper, we study large-scale article access data of the English Wikipedia in order to compare articles with respect to the two main paradigms of information seeking, i.e., search by formulating a query, and navigation by following hyperlinks. To this end, we propose and employ two main metrics, namely (i) searchshare -- the relative amount of views an article received by search --, and (ii) resistance -- the ability of an article to relay traffic to other Wikipedia articles -- to characterize articles. We demonstrate how articles in distinct topical categories differ substantially in terms of these properties. For example, architecture-related articles are often accessed through search and are simultaneously a "dead end" for traffic, whereas historical articles about military events are mainly navigated. We further link traffic differences to varying network, content, and editing activity features. Lastly, we measure the impact of the article properties by modeling access behavior on articles with a gradient boosting approach. The results of this paper constitute a step towards understanding human information seeking behavior on the Web.

cs.SI↗

arXiv2017

MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data

Sequential traces of user data are frequently observed online and offline, e.g., as sequences of visited websites or as sequences of locations captured by GPS. However, understanding factors explaining the production of sequence data is a challenging task, especially since the data generation is often not homogeneous. For example, navigation behavior might change in different phases of browsing a website, or movement behavior may vary between groups of users. In this work, we tackle this task and propose MixedTrails, a Bayesian approach for comparing the plausibility of hypotheses regarding the generative processes of heterogeneous sequence data. Each hypothesis is derived from existing literature, theory or intuition and represents a belief about transition probabilities between a set of states that can vary between groups of observed transitions. For example, when trying to understand human movement in a city and given some observed data, a hypothesis assuming tourists to be more likely to move towards points of interests than locals, can be shown to be more plausible than a hypothesis assuming the opposite. Our approach incorporates such hypotheses as Bayesian priors in a generative mixed transition Markov chain model, and compares their plausibility utilizing Bayes factors. We discuss analytical and approximate inference methods for calculating the marginal likelihoods for Bayes factors, give guidance on interpreting the results, and illustrate our approach with several experiments on synthetic and empirical data from Wikipedia and Flickr. Thus, this work enables a novel kind of analysis for studying sequential data in many application areas.

cs.SI↗

arXiv2017

Analysing Timelines of National Histories across Wikipedia Editions: A Comparative Computational Approach

Portrayals of history are never complete, and each description inherently exhibits a specific viewpoint and emphasis. In this paper, we aim to automatically identify such differences by computing timelines and detecting temporal focal points of written history across languages on Wikipedia. In particular, we study articles related to the history of all UN member states and compare them in 30 language editions. We develop a computational approach that allows to identify focal points quantitatively, and find that Wikipedia narratives about national histories (i) are skewed towards more recent events (recency bias) and (ii) are distributed unevenly across the continents with significant focus on the history of European countries (Eurocentric bias). We also establish that national historical timelines vary across language editions, although average interlingual consensus is rather high. We hope that this paper provides a starting point for a broader computational analysis of written history on Wikipedia and elsewhere.

cs.DL↗

arXiv2017

Why We Read Wikipedia

Wikipedia is one of the most popular sites on the Web, with millions of users relying on it to satisfy a broad range of information needs every day. Although it is crucial to understand what exactly these needs are in order to be able to meet them, little is currently known about why users visit Wikipedia. The goal of this paper is to fill this gap by combining a survey of Wikipedia readers with a log-based analysis of user activity. Based on an initial series of user surveys, we build a taxonomy of Wikipedia use cases along several dimensions, capturing users' motivations to visit Wikipedia, the depth of knowledge they are seeking, and their knowledge of the topic of interest prior to visiting Wikipedia. Then, we quantify the prevalence of these use cases via a large-scale user survey conducted on live Wikipedia with almost 30,000 responses. Our analyses highlight the variety of factors driving users to Wikipedia, such as current events, media coverage of a topic, personal curiosity, work or school assignments, or boredom. Finally, we match survey responses to the respondents' digital traces in Wikipedia's server logs, enabling the discovery of behavioral patterns associated with specific use cases. For instance, we observe long and fast-paced page sequences across topics for users who are bored or exploring randomly, whereas those using Wikipedia for work or school spend more time on individual articles focused on topics such as science. Our findings advance our understanding of reader motivations and behavior on Wikipedia and can have implications for developers aiming to improve Wikipedia's user experience, editors striving to cater to their readers' needs, third-party services (such as search engines) providing access to Wikipedia content, and researchers aiming to build tools such as recommendation engines.

cs.SI↗

arXiv2017

What Makes a Link Successful on Wikipedia?

While a plethora of hypertext links exist on the Web, only a small amount of them are regularly clicked. Starting from this observation, we set out to study large-scale click data from Wikipedia in order to understand what makes a link successful. We systematically analyze effects of link properties on the popularity of links. By utilizing mixed-effects hurdle models supplemented with descriptive insights, we find evidence of user preference towards links leading to the periphery of the network, towards links leading to semantically similar articles, and towards links in the top and left-side of the screen. We integrate these findings as Bayesian priors into a navigational Markov chain model and by doing so successfully improve the model fits. We further adapt and improve the well-known classic PageRank algorithm that assumes random navigation by accounting for observed navigational preferences of users in a weighted variation. This work facilitates understanding navigational click behavior and thus can contribute to improving link structures and algorithms utilizing these structures.

cs.SI↗

arXiv2016

Inferring Gender from Names on the Web: A Comparative Evaluation of Gender Detection Methods

Computational social scientists often harness the Web as a "societal observatory" where data about human social behavior is collected. This data enables novel investigations of psychological, anthropological and sociological research questions. However, in the absence of demographic information, such as gender, many relevant research questions cannot be addressed. To tackle this problem, researchers often rely on automated methods to infer gender from name information provided on the web. However, little is known about the accuracy of existing gender-detection methods and how biased they are against certain sub-populations. In this paper, we address this question by systematically comparing several gender detection methods on a random sample of scientists for whom we know their full name, their gender and the country of their workplace. We further suggest a novel method that employs web-based image retrieval and gender recognition in facial images in order to augment name-based approaches. Our findings show that the performance of name-based gender detection approaches can be biased towards countries of origin and such biases can be reduced by combining name-based an image-based gender detection methods.

cs.CY↗

arXiv2016

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 understanding these clusters, we propose to use HypTrails, a Bayesian approach for expressing and comparing hypotheses about human trails. To formalize hypotheses we utilize data that is publicly available on the Web, namely Foursquare data and census data provided by an open data platform. By applying this combination of approaches to taxi data in Manhattan, we can discover and explain different patterns in human mobility that cannot be identified in a collective analysis. As one example, we can find a group of taxi rides that end at locations with a high number of party venues (according to Foursquare) on weekend nights. Overall, our work demonstrates that human mobility is not one-dimensional but rather contains different facets both in time and space which we explain by utilizing online data. The findings of this paper argue for a more fine-grained analysis of human mobility in order to make more informed decisions for e.g., enhancing urban structures, tailored traffic control and location-based recommender systems.

cs.SI↗