Wouter Weerkamp

Adding semantics to microblog posts

Microblogs have become an important source of information for the purpose of marketing, intelligence, and reputation management. Streams of microblogs are of great value because of their direct and real-time nature. Determining what an individual microblog post is about, however, can be non-trivial because of creative language usage, the highly contextualized and informal nature of microblog posts, and the limited length of this form of communication. We propose a solution to the problem of determining what a microblog post is about through semantic linking: we add semantics to posts by automatically identifying concepts that are semantically related to it and generating links to the corresponding Wikipedia articles. The identified concepts can subsequently be used for, e.g., social media mining, thereby reducing the need for manual inspection and selection. Using a purpose-built test collection of tweets, we show that recently proposed approaches for semantic linking do not perform well, mainly due to the idiosyncratic nature of microblog posts. We propose a novel method based on machine learning with a set of innovative features and show that it is able to achieve significant improvements over all other methods, especially in terms of precision.

Incorporating query expansion and quality indicators in searching microblog posts

We propose a retrieval model for searching microblog posts for a given topic of interest. We develop a language modeling approach tailored to microblogging characteristics, where redundancy-based IR methods cannot be used in a straightforward manner. We enhance this model with two groups of quality indicators: textual and microblog specific. Additionally, we propose a dynamic query expansion model for microblog post retrieval. Experimental results on Twitter data reveal the usefulness of boolean search, and demonstrate the utility of quality indicators and query expansion in microblog search

News comments: exploring, modeling, and online prediction

Online news agents provide commenting facilities for their readers to express their opinions or sentiments with regards to news stories. The number of user supplied comments on a news article may be indicative of its importance, interestingness, or impact. We explore the news comments space, and compare the log-normal and the negative binomial distributions for modeling comments from various news agents. These estimated models can be used to normalize raw comment counts and enable comparison across different news sites. We also examine the feasibility of online prediction of the number of comments, based on the volume observed shortly after publication. We report on solid performance for predicting news comment volume in the long run, after short observation. This prediction can be useful for identifying news stories with the potential to “take off,” and can be used to support front page optimization for news sites.

Bloggers as experts: feed distillation using expert retrieval models

We address the task of (blog) feed distillation: to find blogs that are principally devoted to a given topic. The task may be viewed as an association finding task, between topics and bloggers. Under this view, it resembles the expert finding task, for which a range of models have been proposed. We adopt two language modeling-based approaches to expert finding, and determine their effectiveness as feed distillation strategies. The two models capture the idea that a human will often search for key blogs by spotting highly relevant posts (the Posting model) or by taking global aspects of the blog into account (the Blogger model). Results show the Blogger model outperforms the Posting model and delivers state-of-the art performance, out-of-the-box.

Credibility-inspired ranking for blog post retrieval

Credibility of information refers to its believability or the believability of its sources. We explore the impact of credibility-inspired indicators on the task of blog post retrieval, following the intuition that more credible blog posts are preferred by searchers. Based on a previously introduced credibility framework for blogs, we define several credibility indicators, and divide them into post-level (e.g., spelling, timeliness, document length) and blog-level (e.g., regularity, expertise, comments) indicators. The retrieval task at hand is precision-oriented, and we hypothesize that the use of credibility-inspired indicators will positively impact precision. We propose to use ideas from the credibility framework in a reranking approach to the blog post retrieval problem: We introduce two simple ways of reranking the top n of an initial run. The first approach, Credibility-inspired reranking, simply reranks the top n of a baseline based on the credibility-inspired score. The second approach, Combined reranking, multiplies the credibility-inspired score of the top n results by their retrieval score, and reranks based on this score. Results show that Credibility-inspired reranking leads to larger improvements over the baseline than Combined reranking, but both approaches are capable of improving over an already strong baseline. For Credibility-inspired reranking the best performance is achieved using a combination of all post-level indicators. Combined reranking works best using the post-level indicators combined with comments and pronouns. The blog-level indicators expertise, regularity, and coherence do not contribute positively to the performance, although analysis shows that they can be useful for certain topics. Additional analysis shows that a relative small value of n (15–25) leads to the best results, and that posts that move up the ranking due to the integration of reranking based on credibility-inspired indicators do indeed appear to be more credible than the ones that go down.

A framework for unsupervised spam detection in social networking sites

Social networking sites offer users the option to submit user spam reports for a given message, indicating this message is inappropriate. In this paper we present a framework that uses these user spam reports for spam detection. The framework is based on the HITS web link analysis framework and is instantiated in three models. The models subsequently introduce propagation between messages reported by the same user, messages authored by the same user, and messages with similar content. Each of the models can also be converted to a simple semi-supervised scheme. We test our models on data from a popular social network and compare the models to two baselines, based on message content and raw report counts. We find that our models outperform both baselines and that each of the additions (reporters, authors, and similar messages) further improves the performance of the framework.

Learning to Explain Entity Relationships in Knowledge Graphs

We study the problem of explaining relationships between pairs of knowledge graph entities with human-readable descriptions. Our method extracts and enriches sentences that refer to an entity pair from a corpus and ranks the sentences according to how well they describe the relationship between the entities. We model this task as a learning to rank problem for sentences and employ a rich set of features. When evaluated on a large set of manually annotated sentences, we find that our method significantly improves over state-of-the-art baseline models.

Adaptive temporal query modeling

We present an approach to query modeling that uses the temporal distribution of documents in an initially retrieved set of documents. Such distributions tend to exhibit bursts, especially in news-related document collections. We hypothesize that documents in those bursts are more likely to be relevant and update the query model with the most distinguishing terms in high-quality documents sampled from bursts. We evaluate the effectiveness of our models on a test collection of blog posts.

Pseudo test collections for training and tuning microblog rankers

Recent years have witnessed a persistent interest in generating pseudo test collections, both for training and evaluation purposes. We describe a method for generating queries and relevance judgments for microblog search in an unsupervised way. Our starting point is this intuition: tweets with a hashtag are relevant to the topic covered by the hashtag and hence to a suitable query derived from the hashtag. Our baseline method selects all commonly used hashtags, and all associated tweets as relevance judgments; we then generate a query from these tweets. Next, we generate a timestamp for each query, allowing us to use temporal information in the training process. We then enrich the generation process with knowledge derived from an editorial test collection for microblog search. We use our pseudo test collections in two ways. First, we tune parameters of a variety of well known retrieval methods on them. Correlations with parameter sweeps on an editorial test collection are high on average, with a large variance over retrieval algorithms. Second, we use the pseudo test collections as training sets in a learning to rank scenario. Performance close to training on an editorial test collection is achieved in many cases. Our results demonstrate the utility of tuning and training microblog search algorithms on automatically generated training material.

An effective coherence measure to determine topical consistency in user-generated content

When searching for blogs on a specific topic, information seekers prefer blogs that place a central focus on that topic over blogs whose mention of the topic is diffuse or incidental. In order to present users with better blog feed search results, we developed a measure of topical consistency that is able to capture whether or not a blog is topically focused. The measure, called the coherence score, is inspired by the genetics literature and captures the tightness of the clustering structure of a data set relative to a background collection. In a set of experiments on synthetic data, the coherence score is shown to provide a faithful reflection of topic clustering structure. The properties that make the coherence score more appropriate than lexical cohesion, a common measure of topical structure, are discussed. Retrieval experiments show that integrating the coherence score as a prior in a language modeling-based approach to blog feed search improves retrieval effectiveness. The coherence score must, however, be used judiciously in order to avoid boosting the ranking of irrelevant but topically focused blogs. To this end, we experiment with a series of weighting schemes that adjust the contribution of the coherence score according to the relevance of a blog to the user query. An appropriate weighting scheme is able to improve retrieval performance. Finally, we show that the coherence score can be reliably estimated with a sample exceeding 20 posts in size. Consistent with this finding, experiments show that the best retrieval performance is achieved if coherence scores are used when a blog contains more than 20 posts.