Elisabeth Lex

Blog credibility ranking by exploiting verified content

People use weblogs to express thoughts, present ideas and share knowledge. However, weblogs can also be misused to influence and manipulate the readers. Therefore the credibility of a blog has to be validated before the available information is used for analysis. The credibility of a blogentry is derived from the content, the credibility of the author or blog itself, respectively, and the external references or trackbacks. In this work we introduce an additional dimension to assess the credibility, namely the quantity structure. For our blog analysis system we derive the credibility therefore from two dimensions. Firstly, the quantity structure of a set of blogs and a reference corpus is compared and secondly, we analyse each separate blog content and examine the similarity with a verified news corpus. From the content similarity values we derive a ranking function. Our evaluation showed that one can sort out incredible blogs by quantity structure without deeper analysis. Besides, the content based ranking function sorts the blogs by credibility with high accuracy. Our blog analysis system is therefore capable of providing credibility levels per blog.

Measuring the quality of web content using factual information

Nowadays, many decisions are based on information found in the Web. For the most part, the disseminating sources are not certified, and hence an assessment of the quality and credibility of Web content became more important than ever. With factual density we present a simple statistical quality measure that is based on facts extracted from Web content using Open Information Extraction. In a first case study, we use this measure to identify featured/good articles in Wikipedia. We compare the factual density measure with word count, a measure that has successfully been applied to this task in the past. Our evaluation corroborates the good performance of word count in Wikipedia since featured/good articles are often longer than non-featured. However, for articles of similar lengths the word count measure fails while factual density can separate between them with an F-measure of 90.4%. We also investigate the use of relational features for categorizing Wikipedia articles into featured/good versus non-featured ones. If articles have similar lengths, we achieve an F-measure of 86.7% and 84% otherwise.

A Novel Visualization Approach for Data-Mining-Related Classification

Classification and categorization are common tasks in data mining and knowledge discovery. Visualizations of classification models can create understanding and trust in data mining models. However, existing visualizations are often complex or restricted to specific classifiers and attributes. In this work, we propose an intuitive visualization system to observe and understand classification processes and results. Our system can handle multiple classes, nominal and numeric attributes, and supports all classifiers whose predictions can be interpreted as probabilities. We state that the possibility to observe the training process of a classifier boosts the understanding of classification results also for non-expert users. In combination with an intuitive visualization, we provide a system to generate in-depth understanding of classification processes and results. Our simulations revealed that the system could support the user to better understand a classifiers decision, and to gain insights into classification processes.

Evaluating Tag Recommender Algorithms in Real-World Folksonomies: A Comparative Study

To date, the evaluation of tag recommender algorithms has mostly been conducted in limited ways, including p-core pruned datasets, a small set of compared algorithms and solely based on recommender accuracy. In this study, we use an open-source evaluation framework to compare a rich set of state-of-the-art algorithms in six unfiltered, open datasets via various metrics, measuring not only accuracy but also the diversity, novelty and computational costs of the approaches. We therefore provide a transparent and reproducible tag recommender evaluation in real-world folksonomies. Our results suggest that the efficacy of an algorithm highly depends on the given needs and thus, they should be of interest to both researchers and developers in the field of tag-based recommender systems.

Objectivity classification in online media

In this work, we assess objectivity in online news media. We propose to use topic independent features and we show in a cross-domain experiment that with standard bag-of-word models, classifiers implicitly learn topics. Our experiments revealed that our methodology can be applied across different topics with consistent classification performance.

Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach

Hashtags have become a powerful tool in social platforms such as Twitter to categorize and search for content, and to spread short messages across members of the social network. In this paper, we study temporal hashtag usage practices in Twitter with the aim of designing a cognitive-inspired hashtag recommendation algorithm we call BLLi,s. Our main idea is to incorporate the effect of time on (i) individual hashtag reuse (i.e., reusing own hashtags), and (ii) social hashtag reuse (i.e., reusing hashtags, which has been previously used by a followee) into a predictive model. For this, we turn to the Base-Level Learning (BLL) equation from the cognitive architecture ACT-R, which accounts for the time-dependent decay of item exposure in human memory. We validate BLLI,S using two crawled Twitter datasets in two evaluation scenarios. Firstly, only temporal usage patterns of past hashtag assignments are utilized and secondly, these patterns are combined with a content-based analysis of the current tweet. In both evaluation scenarios, we find not only that temporal effects play an important role for both individual and social hashtag reuse but also that our BLLI,S approach provides significantly better prediction accuracy and ranking results than current state-of-the-art hashtag recommendation methods.

KnowBrain: An Online Social Knowledge Repository for Informal Workplace Learning

We present KnowBrain (KB), an open source Dropbox-like knowledge repository with social features for informal workplace learning. KB enables users (i) to share and collaboratively structure knowledge (ii) to access knowledge via sophisticated content- and metadata-based search and recommendation, and (iii) to discuss artefacts by means of multimedia-enriched Q&A. As such, KB can support, integrate and foster various collaborative learning processes related to daily work-tasks.

A sliding window approach to natural hand gesture recognition using a custom data glove

This paper explores the recognition of hand gestures based on a data glove equipped with motion, bending and pressure sensors. We selected 31 natural and interaction-oriented hand gestures that can be adopted for general-purpose control of and communication with computing systems. The data glove is custom-built, and contains 13 bend sensors, 7 motion sensors, 5 pressure sensors and a magnetometer. We present the data collection experiment, as well as the design, selection and evaluation of a classification algorithm. As we use a sliding window approach to data processing, our algorithm is suitable for stream data processing. Algorithm selection and feature engineering resulted in a combination of linear discriminant analysis and logistic regression with which we achieve an accuracy of over 98.5% on a continuous data stream scenario. When removing the computationally expensive FFT-based features, we still achieve an accuracy of 98.2%.

Attention Please! A Hybrid Resource Recommender Mimicking Attention-Interpretation Dynamics

Classic resource recommenders like Collaborative Filtering (CF) treat users as being just another entity, neglecting non-linear user-resource dynamics shaping attention and interpretation. In this paper, we propose a novel hybrid recommendation strategy that refines CF by capturing these dynamics. The evaluation results reveal that our approach substantially improves CF and, depending on the dataset, successfully competes with a computationally much more expensive Matrix Factorization variant.

Which Algorithms Suit Which Learning Environments? A Comparative Study of Recommender Systems in TEL

In recent years, a number of recommendation algorithms have been proposed to help learners find suitable learning resources on-line. Next to user-centered evaluations, offline-datasets have been used to investigate new recommendation algorithms or variations of collaborative filtering approaches. However, a more extensive study comparing a variety of recommendation strategies on multiple TEL datasets is missing. In this work, we contribute with a data-driven study of recommendation strategies in TEL to shed light on their suitability for TEL datasets. To that end, we evaluate six state-of-the-art recommendation algorithms for tag and resource recommendations on six empirical datasets: a dataset from European Schoolnets TravelWell, a dataset from the MACE portal, which features access to meta-data-enriched learning resources from the field of architecture, two datasets from the social bookmarking systems BibSonomy and CiteULike, a MOOC dataset from the KDD challenge 2015, and Aposdle, a small-scale workplace learning dataset. We highlight strengths and shortcomings of the discussed recommendation algorithms and their applicability to the TEL datasets. Our results demonstrate that the performance of the algorithms strongly depends on the properties and characteristics of the particular dataset. However, we also find a strong correlation between the average number of users per resource and the algorithm performance. A tag recommender evaluation experiment reveals that a hybrid combination of a cognitive-inspired and a popularity-based approach consistently performs best on all TEL datasets we utilized in our study.