Search

This study introduces methods for identifying search directives—content that could prompt an online search—and explores their presence on social media. Search directives can be an effective tool for indirect online influence, because instead of guiding people directly to content (e.g., a news article), they indirectly guide people to it through an independent intermediary (e.g., Google Search). By directing viewers to “do their own research” on a known, trusted, or seemingly objective intermediary, search directives have advantages in terms of both persuasion, by making people feel as if they discovered the content on their own, and evasion, by not directly posting the target link(s). Here we present a framework for identifying search directives on social media that includes methods for automated discovery, classification, and query extraction. Data we collected from social media demonstrate the widespread use of search directives, provide construct validity for our definition, and support our framing of search directives as a form of indirect online influence. Last, we report three case studies that highlight the immediate value of identifying search directives to researchers, practitioners, and journalists focused on online trust and safety. [ABSTRACT FROM AUTHOR]

With advances in artificial intelligence and semantic technology, search engines are integrating semantics to address complex search queries to improve the results. This requires identification of well-known concepts or entities and their relationship from web page contents. But the increase in complex unstructured data on web pages has made the task of concept identification overly complex. Existing research focuses on entity recognition from the perspective of linguistic structures such as complete sentences and paragraphs, whereas a huge part of the data on web pages exists as unstructured text fragments enclosed in HTML tags. Ontologies provide schemas to structure the data on the web. However, including them in the web pages requires additional resources and expertise from organizations or webmasters and thus becoming a major hindrance in their large-scale adoption. We propose an approach for autonomous identification of entities from short text present in web pages to populate semantic models based on a specific ontology model. The proposed approach has been applied to a public dataset containing academic web pages. We employ a long short-term memory (LSTM) deep learning network and the random forest machine learning algorithm to predict entities. The proposed methodology gives an overall accuracy of 0.94 on the test dataset, indicating a potential for automated prediction even in the case of a limited number of training samples for various entities, thus, significantly reducing the required manual workload in practical applications.

This essay discusses the concept of discovery, intended as content discovery, and defines it in the new context of Open Science, with a focus on Social Sciences and Humanities (SSH). Starting from the example of Google Scholar, the authors show that this well established service does not address the current needs, practices, and variety of discovery. Alternatives in terms of technical choices, features, and governance, do however exist, offering richer and more open discovery. The paper presents in particular the implementations and research work of the H2020 project TRIPLE (Transforming Research through Innovative Practices for Linked Interdisciplinary Exploration). Dedicated to the building of a discovery platform for the SSH, the project is meant to address the specificities and evolution of discovery in this field. Prevailing scholarly resource platforms like Google Scholar limit discovery by focussing only on publications, and favouring through their algorithm well-cited papers, English content, and discipline-specific resources. A limitation in the context of cross-disciplinary and collaborative Open Science, such a service more specifically hinders discovery in the SSH. Characterized by a fragmented landscape, a variety of languages, data types, and outputs, research in the SSH requires services that fully exploit discovery potentialities. Moreover, a survey conducted within the TRIPLE project showed that most SSH researchers use Google Scholar as their starting point, and that they recognise the lack of control they have with this system. Beyond the extension of features and content, transparency is the other important criterion for the building of an Open Infrastructure actually serving the research community. In light of this, we present in some detail the GoTriple platform, which exploits today’s technological potential and incorporates the best known functionalities in order to unveil more and innovative scholarly outputs and lead to international and interdisciplinary research project collaborations.

One of the key challenges in ad-hoc networks is the resource discovery problem. How efficiently & quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question? Broadcasting is a basic technique in the Mobile Ad-hoc Networks (MANETs), and it refers to sending a packet from one node to every other node within the transmission range. Flooding is a type of broadcast where the received packet is retransmitted once by every node. The naive fooding technique floods the network with query messages, while the random walk scheme operates by contacting subsets of each node's neighbors at every step, thereby restricting the search space. Many earlier works have mainly focused on the simulation-based analysis of flooding technique, and its variants, in a wired network scenario. Although, there have been some empirical studies in peer-to-peer (P2P) networks, the analytical results are still lacking, especially in the context of mobile P2P networks. In this article, we mathematically model different widely used existing search techniques, and compare with the proposed improved random walk method, a simple lightweight approach suitable for the non-DHT architecture. We provide analytical expressions to measure the performance of the different flooding-based search techniques, and our proposed technique. We analytically derive 3 relevant key performance measures, i.e., the avg. number of steps needed to find a resource, the probability of locating a resource, and the avg. number of messages generated during the entire search process. [ABSTRACT FROM AUTHOR]

Resource discovery is a key challenge in dynamic environment such as Peer-to-Peer (P2P) MANETs. To leverage the lookup costs and efficiently discover the resources, the peers in a P2P network communicate with each other forming one-or-more overlay structure. And, thus the peer's connections in such overlay networks plays a crucial role. To harness this, we propose a model that focuses on the underlying network topology as each virtual link of the overlay network is supported by a path in the underlying physical network. Also, we design a new resource discovery algorithm that uses the famous Rock-Paper-Scissors (RPS) game ideology. In this work, we present a Rock-Paper-Scissors-Game-Based (RPSGB) algorithm for content discovery in mobile peer-to-peer network. The proposed work focuses on providing efficient resource discovery scheme in such a dynamic network, using the game theory concepts. Our scheme is a light-weight model aimed to suit the unstructured architecture better. The major goal of this work, is to reduce the consumption of power, minimize the lookup cost, lower the bandwidth consumption, and increase the hit ratio. We compare our scheme with the traditional well-known benchmarked schemes. After evaluation, the simulation results justify the effectiveness of the proposed protocol. The obtained results show that the proposed scheme substantially decreases the network traffic, lowers the battery power and bandwidth consumption, while having good search efficiency. Also, the search latency is minimized. The result justifies that RPSGB algorithm proposes to make resource searching much more efficient, and improves the statistics against the posed challenges. [ABSTRACT FROM AUTHOR]

Online Social Network Sites have become a primary platform for brands and organizations to engage their audience by sharing image and video posts on their timelines. Different from traditional advertising, these posts are not restricted to the products or logo but include visual elements that express more in general the values and attributes of the brand, called brand associations. Since marketers are increasingly spending time in discovering and re-posting user generated posts that reflect the brand attributes, there is an increasing demand for such discovery systems. The goal of these systems is to assist brand experts in filtering through online collections of new user media to discover actionable posts, which match the brand value and have the potential to engage the consumers. Driven by this real-life application, we define and formulate a new task of content discovery for brands and propose a framework that learns to rank posts for brands from their historical timeline. We design a Personalized Content Discovery (PCD) framework to address the three challenges of high inter-brand similarity, sparsity of brand-post interactions, and diversification of timeline. To learn finegrained brand representation and to generate explanations for the ranking, we automatically learn visual elements of posts from the timeline of brands and from a set of brand attributes in the domain of marketing. To test our framework we use two large-scale Instagram datasets that contain a total of more than 1.5 million image and video posts from the historical timeline of hundreds of brands from multiple verticals such as food and fashion. Extensive experiments indicate that our model can effectively learn fine-grained brand representations and outperform the closest state-of-the-art solutions. [ABSTRACT FROM AUTHOR]

The pervasiveness of social media and user-generated content has triggered an exponential increase in global data. However, due to collection and extraction challenges, data in embedded comments, reviews and testimonials are largely inaccessible to a knowledge management system. This article describes a KM framework for the end-to-end knowledge management and value extraction from such content. This framework embodies solutions to unlock the potential of UGC as a rich, real-time data source. Three contributions are described in this article. First, a method for automatically navigating webpages to expose UGC for collection is presented. This is evaluated using browser emulation integrated with automated collection. Second, a method for collecting data without any a priori knowledge of the sites is introduced. Finally, a new testbed is developed to reflect the current state of internet sites and shared publicly to encourage future research. The discussion benchmarks the new algorithm alongside existing techniques, providing evidence of the increased amount of UGC data extracted. [ABSTRACT FROM AUTHOR]

The explosion of traffic demands in the edge of the Internet, mostly by mobile users, is putting under pressure current networking infrastructures. This is particularly acute when huge amounts of users and active wireless devices gather in reduced geographical spaces, increasing the risk of exceeding planned capacity of deployed infrastructure. This trend motivates research on edge computing, and in particular, on mechanisms to offload or address locally part of the user injected traffic at the access infrastructure, thus reducing the need of Internet requests and retrievals. This paper concentrates on the ability of mobile crowds –and corresponding access networks—to fulfill content requests originated within the mesh, with minimal intervention of the Internet infrastructure. Simple heuristics are revisited, proposed, discussed and evaluated to improve autonomous content discovery and dissemination within high-density, low-mobility crowds, by combining notions already explored for MANET routing: deliberate jittering and autonomous distance-based overlay pruning. Results over synthetic networks and real mobility traces indicate that these mechanisms improve efficiency and quality of content request discoveries, by reducing significantly collisions and increasing stability of discovered paths in dense pedestrian crowds. [ABSTRACT FROM AUTHOR]

Named data networking (NDN) aims at efficient content delivery and reducing the redundancy of data transmission. In NDN, one of the most important issues is how to utilize the cached contents to reduce the user's response delay and improve the utilization of the cache resources. In this paper, an intelligent content discovery system to fully utilize the cache resources, called DENA, is proposed, which encompasses the deep exponential network-based cache announcement and cache exergy-based cache replacement algorithms. DENA provides shortcut paths for interest packets to access targeted data packets under instructions of AT (announcement table) constructed by a precision cached content announcement algorithm with low communication overhead. In the meantime, a cache replacement algorithm to assist the cache announcement is also proposed. The method uses a so-called cache exergy index to instruct the replacement of cached content. The simulation results indicate that the system improves the utilization of cache resources and the network capacity while keeping the network control traffic at a relatively low level.

Recent disasters, such as the 2010 Haiti earthquake, have drawn attention to the potential role of citizens as active information producers. By using location-aware devices such as smartphones to collect geographic information in the form of geo-tagged text, photos, or videos, and sharing this information through online social media, such as Twitter, citizens create Volunteered Geographic Information (VGI). To effectively use this information for disaster management, we developed a VGI framework for the discovery of VGI. This framework consists of four components: (i) a VGI brokering module to provide a standard service interface to retrieve VGI from multiple resources based on spatial, temporal, and semantic parameters; (ii) a VGI quality control component, which employs semantic filtering and cross-referencing techniques to evaluate VGI; (iii) a VGI publisher module, which uses a service-based delivery mechanism to disseminate VGI, and (iv) a VGI discovery component to locate, browse, and query metadata about available VGI datasets. In a case study we employed a FOSS (Free and Open Source Software) strategy, open standards/specifications, and free/open data to show the utility of the framework. We demonstrate that the framework can facilitate data discovery for disaster management. The addition of quality metrics and a single aggregated source of relevant crisis VGI will allow users to make informed policy choices that could save lives, meet basic humanitarian needs earlier, and perhaps limit environmental and economic damage.

The basic concepts of graph databases and knowledge graphs are presented, based on the case study of the digital semantic model of the Museo del Prado, and the impact on the redesign of its website, focused on the enriched access to its collection and linked resources, and metadata for content discovery. [ABSTRACT FROM AUTHOR]

Scoped-flooding is used for content discovery in a broad networking context and it has significant impact on the design of caching algorithms in a communication network. Despite its wide usage, a thorough analysis on how scoped-flooding affects a network’s performance, e.g., caching and content discovery efficiency, is missing. To develop a better understanding, we first model the behavior of scoped-flooding by the help of a theoretical model on network growth and utility. Next, we investigate the effects of scoped-flooding on various topologies in information-centric networks (ICNs). Using the proposed ring model, we show that flooding can be constrained within a small neighborhood to achieve most of the gains which come from areas with relatively low growth rate, i.e., the network edge. We also study two flooding strategies and compare their behaviors. Given that caching schemes favor more popular items in competition for cache space, popular items are expected to be stored in diverse parts of the network compared to the less popular items. We propose to exploit the resulting divergence in availability along with the routers’ topological properties to fine tune the flooding radius. Our results shed light on designing both efficient content discovery mechanism and effective caching algorithms for future ICN. [ABSTRACT FROM AUTHOR]

Recent disasters, such as the 2010 Haiti earthquake, have drawn attention to the potential role of citizens as active information producers. By using location-aware devices such as smartphones to collect geographic information in the form of geo-tagged text, photos, or videos, and sharing this information through online social media, such as Twitter, citizens create Volunteered Geographic Information (VGI). To effectively use this information for disaster management, we developed a VGI framework for the discovery of VGI. This framework consists of four components: (i) a VGI brokering module to provide a standard service interface to retrieve VGI from multiple resources based on spatial, temporal, and semantic parameters; (ii) a VGI quality control component, which employs semantic filtering and cross-referencing techniques to evaluate VGI; (iii) a VGI publisher module, which uses a service-based delivery mechanism to disseminate VGI, and (iv) a VGI discovery component to locate, browse, and query metadata about available VGI datasets. In a case study we employed a FOSS (Free and Open Source Software) strategy, open standards/specifications, and free/open data to show the utility of the framework. We demonstrate that the framework can facilitate data discovery for disaster management. The addition of quality metrics and a single aggregated source of relevant crisis VGI will allow users to make informed policy choices that could save lives, meet basic humanitarian needs earlier, and perhaps limit environmental and economic damage. [ABSTRACT FROM AUTHOR]

Content discovery is a major source of latency in peer-to-peer (P2P) media streaming systems, especially in the presence of noncontinuous user access, such as random seek in Video-on-Demand (VoD) streaming and teleportation in a Networked Virtual Environment (NVE). After the aforementioned user interactions, streaming systems often need to initiate the content discovery process to identify where to retrieve the requested media objects. Short content lookup latency is demanded to ensure smooth user experience. Existing content discovery systems based on either a Distributed Hash Table (DHT) or gossip mechanism cannot cope with noncontinuous access efficiently due to their long lookup latency. In this work, we propose an access-pattern-driven distributed caching middleware named APRICOD, which caters for fast and scalable content discovery in peer-to-peer media streaming systems, especially when user interactions are present. APRICOD exploits correlations among media objects accessed by users, and adapts to shift in the user access pattern automatically. We first present a general APRICOD design that can be used with any existing content discovery system. We then present an implementation of APRICOD on top of Pastry, which we use to evaluate APRICOD. Our evaluation in a 1024-node system, using a Second Life trace with 5, 735 users and a VoD trace with 54 users, shows that APRICOD can effectively resolve all continuous access queries with a single hop deterministically with node failure as an exception, and resolve noncontinuous access queries with a single hop with high probability. [ABSTRACT FROM AUTHOR]

We address content discovery in wireless networks with infrastructure, where mobile nodes store, advertise, and consume content while Broker entities running on infrastructure devices let demand and offer meet. We refer to this paradigm as match-making, highlighting its features within the confines of the standard publish-and-subscribe paradigm. We study its performance in terms of success probability of a content query, a parameter that we strive to increase by acting as follows: 1) We design a credit-based scheme that makes it convenient for rational users to provide their content (thus discouraging free-riding behavior), and it guarantees them a fair treatment. 2) We increase the availability of either popular or rare content, through an efficient caching scheme. 3) We counter malicious nodes whose objective is to disrupt the system performance by not providing the content they advertise. To counter the latter as well as free riders, we introduce a feedback mechanism that enables a Broker to tell apart well- and misbehaving nodes in a very reliable manner, and to ban the latter. The properties of our match-making scheme are analyzed through game theory. Furthermore, via ns-3 simulations, we show its resilience to different attacks by malicious users and its good performance with respect to other existing solutions. [ABSTRACT FROM AUTHOR]

The design of approaches for supporting the user in the navigation of a variety of contents is an interesting area of research with many potential applications. In particular, interactive television (iTV) offers users the opportunity of accessing a huge amount of contents, ranging from general to specialized ones. As a consequence, the exploration of such contents must be supported in some way. In this paper, we present an innovative approach that integrates some recent methodologies and technologies developed in different areas of Artificial Intelligence and the Web: user-model-based adaptation, social networking, semantic annotation, and content sharing. We show how the integration of these technologies can provide interesting opportunities for a new approach to content navigation and discovery based on the possibility of exploring personalized networks of contents, users and concepts. Also, we focus on the specific goal of designing a platform for accessing iTV contents with the aim of providing the user with many alternative ways of exploring and discovering potentially interesting videos. After discussing the application, integrated in a project by Telecom Italia for a new paradigm of iTV, we preset the architecture we designed for integrating the methodologies discussed above. [ABSTRACT FROM AUTHOR]

In this paper, we seek to provide reliable and fast content discovery in peer-to-peer (P2P) video-on-demand (VoD) system to enable user interactivity under peer dynamics. We first identify two characteristics of content discovery in P2P-VoD: real-time constraints and limited local cache. Tapping on these properties, we propose a hybrid content discovery mechanism: SUpeRchunk- based eFficient search Network (SURFNet). SURFNet divides movies into superchunks and chunks. Stable peers that are likely to have longer lifespans in the system are used to construct an AVL tree to provide superchunk-level data availability information. Peers storing the same superchunk are grouped into a holder-chain, which is then attached to a node in the AVL tree. This structured overlay is further extended by a gossip-based unstructured network for chunk-level information exchange and data transmission. Since the AVL tree consists of only stable peers, it provides a reliable backbone even in highly dynamic environment. Our analysis and simulation results show that SURFNet supports nearly-constant and logarithmic search time for seeking within a video and jumping to a different video, respectively. [ABSTRACT FROM PUBLISHER]