Geometric discriminative features for aerial image retrieval in social media

The aerial image recognition is an important problem in multimedia information retrieval in social media. In this paper, we propose a new approach by integrating aerial image’s local features into a discriminative one which reflects both the geometric property and the color distribution of aerial image. Firstly, each aerial image is segmented into several regions in terms of their color intensities. And region connected graph (RCG), the links between the spatial neighboring regions, is presented to encode the spatial context of aerial images. Secondly, we mine frequent structures in the RCGs corresponding to training aerial images collected from social media. And a set of refined structures are selected among the frequent ones towards being more discriminative and less redundant. Finally, given a new aerial image, its sub-RCGs corresponding to all the refined structures are extracted and quantized into a discriminative feature for aerial image recognition. The experimental results validate the proposed method by providing a more accurate recognition result of the aerial images on different datasets from different social medias.     

Generating and exploiting customer insights from social media data

Previous research has emphasized the virtues of customer insights as a key source of competitive advantage. The rise of customers’ social media use allows firms to collect customer data in an ever-increasing volume and variety. However, to date, little is known about the capabilities required of firms to turn social media data into valuable customer insights and exploit these insights to create added value for customers. Based on the dynamic capabilities perspective, in particular the concept of absorptive capacity (ACAP), the authors conducted multiple case studies of seven mid-sized and large B2C firms in Switzerland and Germany. The results provide an in-depth analysis of the underlying processes of ACAP as well as contingent factors – that is, physical, human and organizational resources that underpin the firms’ ACAP.     

Logo information recognition in large-scale social media data

Recent years have shown us the quick development of social network. For companies, microblog platform is more and more important as one source to disseminate brand information and monitor their development. Compared with the frequently used text information existing in traditional media, microblog platform provides information about brands in more types such as images and other related information forms. According to the statistics, microblogs posted on social network contain more and more percentage of images. Hence how to recognize logos in images from social network is of high value. To address this problem, we propose a novel learning-based logo detection method with social network information assistance. A new dense histogram type feature is proposed to classify logo and non-logo image patches. To increase the detection precision, social network content is analyzed and employed to do filtering to reduce detection window candidates. Through the evaluation on large-scale data collected from Sina Weibo platform, the proposed method is demonstrated effective.     

Social velocity based spatio-temporal anomalous daily activity discovery of social media users

Applied Intelligence - Anomalous daily activities are the activities that do not fit into normal daily behavior of social media users. Discovering anomalous daily activities is important for...     

Feature selection for event discovery in social media: A comparative study

Microblog as one kind of typical social media has many research implications in social event discovery and social-media-based e-learning and collaborative learning. At present, researchers usually employ feature-based classification approaches to detect social events in microblogs. However, it is very common to get different results when different features are used in event discovery. Therefore, it has been a critical issue how to select appropriate features for event discovery in microblogs. In this paper, we analyze five different feature selection methods and present an improved method for selecting features for microblog-based event discovery. We compare all the methods on a real microblog dataset in terms of various metrics including precision, recall, and F-measure. And finally we discuss the best feature selection method for the event discovery in microblogs. To the best of our knowledge, there are no such comparative studies on feature selection for event discovery in social media, and this paper is expected to offer some useful references for the future research and applications on the event discovery in microblogs.     

Recognizing composite daily activities from crowd-labelled social media data

Human activity recognition is a core component of context-aware, ubiquitous computing systems. Traditionally, this task is accomplished by analysing signals of wearable motion sensors. While successful for low-level activities (e.g. walking or standing), high-level activities (e.g. watching movies or attending lectures) are difficult to distinguish from motion data alone. Furthermore, instrumentation of complex body sensor network at population scale is impractical. In this work, we take an alternative approach of leveraging rich, dynamic, and crowd-generated self-report data from social media platforms as the basis for in-situ activity recognition. By treating the user as the “sensor”, we make use of implicit signals emitted from natural use of mobile smartphones, in the form of textual content, semantic location, and time. Tackling both the task of recognizing a main activity (multi-class classification) and recognizing all applicable activity categories (multi-label tagging) from one instance, we are able to obtain mean accuracies of more than 75%. We conduct a thorough analysis and interpret of our model to illustrate a promising first step towards comprehensive, high-level activity recognition using instrumentation-free, crowdsourced, social media data.     

CrimeTelescope: crime hotspot prediction based on urban and social media data fusion

Crime is a complex social issue impacting a considerable number of individuals within a society. Preventing and reducing crime is a top priority in many countries. Given limited policing and crime reduction resources, it is often crucial to identify effective strategies to deploy the available resources. Towards this goal, crime hotspot prediction has previously been suggested. Crime hotspot prediction leverages past data in order to identify geographical areas susceptible of hosting crimes in the future. However, most of the existing techniques in crime hotspot prediction solely use historical crime records to identify crime hotspots, while ignoring the predictive power of other data such as urban or social media data. In this paper, we propose CrimeTelescope, a platform that predicts and visualizes crime hotspots based on a fusion of different data types. Our platform continuously collects crime data as well as urban and social media data on the Web. It then extracts key features from the collected data based on both statistical and linguistic analysis. Finally, it identifies crime hotspots by leveraging the extracted features, and offers visualizations of the hotspots on an interactive map. Based on real-world data collected from New York City, we show that combining different types of data can effectively improve the crime hotspot prediction accuracy (by up to 5.2%), compared to classical approaches based on historical crime records only. In addition, we demonstrate the usability of our platform through a System Usability Scale (SUS) survey on a full prototype of CrimeTelescope.     

Crowdsourcing based social media data analysis of urban emergency events

Multimedia Tools and Applications - An urban emergency event requires an immediate reaction or assistance for an emergency situation. With the popularity of the World Wide Web, the internet is...     

Short-text feature construction and selection in social media data: a survey

Social networking sites such as Facebook or Twitter attract millions of users, who everyday post an enormous amount of content in the form of tweets, comments and posts. Since social network texts are usually short, learning tasks have to deal with a very high dimensional and sparse feature space, in which most features have low frequencies. As a result, extracting useful knowledge from such noisy data is a challenging task, that converts large-scale short-text learning tasks in social environments into one of the most relevant problems in machine learning and data mining. Feature selection is one of the most known and commonly used techniques for reducing the impact of the high dimensional feature space in text learning. A wide variety of feature selection techniques can be found in the literature applied to traditional, long-texts and document collections. However, short-texts coming from the social Web pose new challenges to this well-studied problem as texts’ shortness offers a limited context to extract enough statistical evidence about words relations (e.g. correlation), and instances usually arrive in continuous streams (e.g. Twitter timeline), so that the number of features and instances is unknown, among other problems. This paper surveys feature selection techniques for dealing with short texts in both offline and online settings. Then, open issues and research opportunities for performing online feature selection over social media data are discussed.     

Interactive faceted search and exploration of open social media data on a touchscreen mobile phone

A central challenge of semantic ambient media applications is designing smart user interfaces that are able to dynamically deal with an a-priori unknown number of data categories and data instances received live from different Linked Open Data sources while at the same time being intuitive and easy to use. In the mobile world, this challenge is even more difficult as the mobile devices have limited interaction possibilities and smaller display size. In this paper, we tackle this challenge and present the user-centered, iterative design of a mobile application for faceted search and exploration of a large, multi-dimensional data set of open social media on a touchscreen mobile phone. The application is called Mobile Facets and provides live retrieval and interactive search and exploration of resources like places, persons, organizations, and events originating from different, integrated social media sources like DBpedia, Eventful, Upcoming, Flickr, and GeoNames. In contrast to existing work, we do not know in advance the number and type of data categories and data instances that will be received as the data is queried live from the sources. While developing Mobile Facets, we have applied a participatory design with a small group of five users. For the final prototype we have conducted a task-based, formative evaluation with 12 additional subjects to investigate the applicability and usability of our Mobile Facets application.     

Web data retrieval and extraction

We present the Object-Web Mediator to querying integrated Web data sources composed of a retrieval component based on an intermediate object view mechanism and search views, and an XML engine. Search views map the source capabilities to attributes defined at object classes, and parsers that process retrieved documents and cache them in XML format. The XML engine queries cached documents, extracts data, and returns extracted data for evaluation. The originality of this approach consists of a generic view mechanism to access data sources with limited data access and complex capabilities, and an XML engine to support data extraction and reorganization. This approach has been developed and demonstrated as part of the multi-database system supporting queries via uniform Object Protocol Model interfaces against public Web data sources of interest to the biologists.     

User profiling for big social media data using standing ovation model

Multimedia Tools and Applications - Online Social Networks (OSNs) have recently been the subject of numerous studies that have attempted to develop effective methods for classifying and analyzing...     

Integrating document and data retrieval based on XML

For querying structured and semistructured data, data retrieval and document retrieval are two valuable and complementary techniques that have not yet been fully integrated. In this paper, we introduce integrated information retrieval (IIR), an XML-based retrieval approach that closes this gap. We introduce the syntax and semantics of an extension of the XQuery language called XQuery/IR. The extended language realizes IIR and thereby allows users to formulate new kinds of queries by nesting ranked document retrieval and precise data retrieval queries. Furthermore, we detail index structures and efficient query processing approaches for implementing XQuery/IR. Based on a new identification scheme for nodes in node-labeled tree structures, the extended index structures require only a fraction of the space of comparable index structures that only support data retrieval.     

Visual link retrieval and knowledge discovery in painting datasets

Visual arts are of inestimable importance for the cultural, historic and economic growth of our society. One of the building blocks of most analysis in visual arts is to find similarity relationships among paintings of different artists and painting schools. To help art historians better understand visual arts, this paper presents a framework for visual link retrieval and knowledge discovery in digital painting datasets. Visual link retrieval is accomplished by using a deep convolutional neural network to perform feature extraction and a fully unsupervised nearest neighbor mechanism to retrieve links among digitized paintings. Historical knowledge discovery is achieved by performing a graph analysis that makes it possible to study influences among artists. An experimental evaluation on a database collecting paintings by very popular artists shows the effectiveness of the method. The unsupervised strategy makes the method interesting especially in cases where metadata are scarce, unavailable or difficult to collect.