Tweeting Behaviour during Train Disruptions within a City

In a smart city environment, citizens use social media for communicating and reporting events. Existing work has shown that social media tools, such as Twitter and Facebook, can be used as social sensors to monitor events in real-time as they happen (e.g. riots, natural disasters and sport events). In this paper, we study the reactions of citizens in social media towards train disruptions within a city. Our study using 30 days of tweets in a large city shows that citizens react differently to train disruptions by, for instance, displaying unique behaviours in tweeting depending on the time of the disruption. Specifically, for working days, tweets related to train disruptions are typically generated during rush hour periods. In contrast, during weekends, urban citizens tended to tweet about train disruptions during late evenings. Using these insights, we develop a supervised approach to predict whether a train disruption tweet will be retweeted and propagated on the social network, by using features, such as time, user, and the content of tweets. Our experimental results show that we can effectively predict when a train disruption tweet is retweeted by using such features

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

Challenges in recommending venues within smart cities

Recommending venues to a user within a city is a task that has emerged recently with the growing interest in location-based information access. However, the current applications for this task only use the limited and private data gathered by Location-based Social Networks (LBSNs) such as Foursquare or Google Places. In this position paper, we discuss the research opportunities that can arise with the use of the digital infrastructure of a smart city, and how the venue recommendation applications can benefit from this infrastructure. We focus on the potential applications of social and physical sensors for improving the quality of the recommendations, and highlight the challenges in evaluating such recommendations

RGU-ISTI-Essex at TREC 2011 Session Track

Mining query recommendation from query logs has attracted a lot of attention in recent years. We propose to use query recommendations extracted from the logs of a web search engine to solve the session track tasks. The runs are obtained by using the Search Shortcuts recommender system. The Search Shortcuts technique uses an inverted index and the concept of “successful sessions” present in a web search engine’s query log to produce effective recommendations for both frequent and rare/unseen queries. We adapt the above technique as a query expan- sion tool and use it to expand the given queries for Session Track at TREC 2011. The expansion is generated by using a method which aims to consider all past queries in the session. The expansion terms obtained are then used to build a global, uniformly weighted, representation of the user session (RL2). Furthermore, the expansion terms are then combined with a ranked list of results in order to boost terms appearing more frequently in the final results lists (RL3). Finally, we also integrate dwell times and the weighting method obtained taking both result lists and clicks into account for assigning weights to the terms to expand the final query of the session. In addition to that, we submitted a baseline run. It is based on the observation that using the term “wikipedia” to expand the query resulted in a better retrieval performance for the tasks at last year’s session track at TREC 2010

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

“When Was This Picture Taken?” – Image Date Estimation in the Wild

The problem of automatically estimating the creation date of photos has been addressed rarely in the past. In this paper, we introduce a novel dataset Date Estimation in the Wild for the task of predicting the acquisition year of images captured in the period from 1930 to 1999. In contrast to previous work, the dataset is neither restricted to color photography nor to specific visual concepts. The dataset consists of more than one million images crawled from Flickr and contains a large number of different motives. In addition, we propose two baseline approaches for regression and classification, respectively, relying on state-of-the-art deep convolutional neural networks. Experimental results demonstrate that these baselines are already superior to annotations of untrained humans

“Are Machines Better Than Humans in Image Tagging?” - A User Study Adds to the Puzzle

“Do machines perform better than humans in visual recognition tasks?” Not so long ago, this question would have been considered even somewhat provoking and the answer would have been clear: “No”. In this paper, we present a comparison of human and machine performance with respect to annotation for multimedia retrieval tasks. Going beyond recent crowdsourcing studies in this respect, we also report results of two extensive user studies. In total, 23 participants were asked to annotate more than 1000 images of a benchmark dataset, which is the most comprehensive study in the field so far. Krippendorff’s α is used to measure inter-coder agreement among several coders and the results are compared with the best machine results. The study is preceded by a summary of studies which compared human and machine performance in different visual and auditory recognition tasks. We discuss the results and derive a methodology in order to compare machine performance in multimedia annotation tasks at human level. This allows us to formally answer the question whether a recognition problem can be considered as solved. Finally, we are going to answer the initial question

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

No abstract available