Using Sensor Metadata Streams to Identify Topics of Local Events in the City

In this paper, we study the emerging Information Retrieval (IR) task of local event retrieval using sensor metadata streams. Sensor metadata streams include information such as the crowd density from video processing, audio classifications, and social media activity. We propose to use these metadata streams to identify the topics of local events within a city, where each event topic corresponds to a set of terms representing a type of events such as a concert or a protest. We develop a supervised approach that is capable of mapping sensor metadata observations to an event topic. In addition to using a variety of sensor metadata observations about the current status of the environment as learning features, our approach incorporates additional background features to model cyclic event patterns. Through experimentation with data collected from two locations in a major Spanish city, we show that our approach markedly outperforms an alternative baseline. We also show that modelling background information improves event topic identification

A Methodology for Simulated Experiments in Interactive Search

Interactive information retrieval has received much attention in recent years, e.g. [7]. Furthermore, increased activity in developing interactive features in search systems used across existing popular Web search engines suggests that interactive systems are being recognised as a promising next step in assisting information search. One of the most challenging problems with interactive systems however remains evaluation. We describe the general specifications of a methodology for conducting controlled and reproducible experiments in the context of interactive search. It was developed in the AutoAdapt project1 focusing on search in intranets, but the methodology is more generic than that and can be applied to interactive Web search as well. The goal of this methodology is to evaluate the ability of different algorithms to produce domain models that provide accurate suggestions for query modifications. The AutoAdapt project investigates the application of automatically constructed adaptive domain models for providing suggestions for query modifications to the users of an intranet search engine. This goes beyond static models such as the one employed to guide users who search the Web site of the University of Essex which is based on a domain model that has been built in advance using the documents’ markup structure

Preface

These proceedings contain the papers of the Third International Workshop on Recent Trends in News Informa-tion Retrieval (NewsIR\u201919) held in conjunction with the ACM SIGIR 2019 conference in Paris, France, on the25thof July 2019. Ten full papers and two short papers (one position paper and one demo paper) were selectedby the programme committee from a total of 21 submissions. Each submitted paper was reviewed by at leastthree members of an international programme committee. In addition to the selected papers, the workshopfeatures one keynote and one invited talk. The Keynote speech is given by Aron Pilhofer \u201cFrom Redlining toRobots: How newsrooms apply technology to the craft of journalism\u201d. The invited talk is given by FriedrichLindenberg \u201cMining Leaks and Open Data to Follow the Money\u201d. We would like to thank SIGIR for hostingus. Thanks also go to the keynote speakers, the program committee, the paper authors, and the participants,for without these people there would be no worksho

Moving towards adaptive search in digital libraries

Search applications have become very popular over the last two decades, one of the main drivers being the advent of the Web. Nevertheless, searching on the Web is very different to searching on smaller, often more structured collections such as digital libraries, local Web sites, and intranets. One way of helping the searcher locating the right information for a specific information need in such a collection is by providing well-structured domain knowledge to assist query modification and navigation. There are two main challenges which we will both address in this chapter: acquiring the domain knowledge and adapting it automatically to the specific interests of the user community. We will outline how in digital libraries a domain model can automatically be acquired using search engine query logs and how it can be continuously updated using methods resembling ant colony behaviour. © 2011 Springer-Verlag

Experiments with a Venue-Centric Model for Personalisedand Time-Aware Venue Suggestion

No abstract available

Identifying local events by using microblogs as social sensors

Local search is increasingly attracting more demand, whereby the users are interested to find out about places or events in their local vicinity. In this paper, we propose to use the Twitter microblogging platform to detect and rank local events of interest in real-time. We present a novel event retrieval framework, where both the contents of the tweets and the volume of the microblogging activity are exploited to locate an event happening in a certain area within a city that matches the user's interests as expressed in the form of a query. In particular, the framework measures unusual microblogging activities in a certain area and uses that as an indication of the occurrence of an event which is then used by the ranking function. Since the proposed event retrieval task is a new Information Retrieval (IR) task, we devise a methodology that is inspired by the conceptually similar IR problem of video segmentation to thoroughly evaluate our approach. Our evaluation is conducted on a set of tweets collected over a period of twelve days from different areas of London, as well as two sets of local events collected within the same period using crowdsourcing and local news sources in London. In addition to new insights on the factors that in uence the development of an effective event ranking model, our empirical results show the promise and effectiveness of our proposed approach in identifying and ranking local events in real-time

Do topic shift and query reformulation patterns correlate in academic search?

While it is known that academic searchers differ from typical web searchers, little is known about the search behavior of academic searchers over longer periods of time. In this study we take a look at academic searchers through a large-scale log analysis on a major academic search engine. We focus on two aspects: query reformulation patterns and topic shifts in queries. We first analyze how each of these aspects evolve over time. We identify important query reformulation patterns: revisiting and issuing new queries tend to happen more often over time. We also find that there are two distinct types of users: one type of users becomes increasingly focused on the topics they search for as time goes by, and the other becomes increasingly diversifying. After analyzing these two aspects separately, we investigate whether, and to which degree, there is a correlation between topic shifts and query reformulations. Surprisingly, users’ preferences of query reformulations correlate little with their topic shift tendency. However, certain reformulations may help predict the magnitude of the topic shift that happens in the immediate next timespan. Our results shed light on academic searchers’ information seeking behavior and may benefit search personalization