Digital native advertising:Practitioner perspectives and a research agenda

Digital native advertising is a subtle form of digital advertising that is integrated closely with its context. Practitioners are increasingly assigning budgets to this advertising strategy. On the basis of 22 in-depth expert interviews with senior executives of advertising brands, publishing companies, and media agencies, this study provides new insights into the effectiveness of digital native advertising. We also shed light on factors in the field of content and context of digital native advertising that influence its performance. We present 10 key propositions that reflect practitioners' perspectives and form an agenda for further scientific research in the field of digital native advertising

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience

Application of Common Sense Computing for the Development of a Novel Knowledge-Based Opinion Mining Engine

The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis and other online collaborative media. The distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand, or organisation. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. Hitherto, online information retrieval has been mainly based on algorithms relying on the textual representation of web-pages. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling and counting their words. But when it comes to interpreting sentences and extracting meaningful information, their capabilities are known to be very limited. Existing approaches to opinion mining and sentiment analysis, in particular, can be grouped into three main categories: keyword spotting, in which text is classified into categories based on the presence of fairly unambiguous affect words; lexical affinity, which assigns arbitrary words a probabilistic affinity for a particular emotion; statistical methods, which calculate the valence of affective keywords and word co-occurrence frequencies on the base of a large training corpus. Early works aimed to classify entire documents as containing overall positive or negative polarity, or rating scores of reviews. Such systems were mainly based on supervised approaches relying on manually labelled samples, such as movie or product reviews where the opinionist’s overall positive or negative attitude was explicitly indicated. However, opinions and sentiments do not occur only at document level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a document. In more recent works, text analysis granularity has been taken down to segment and sentence level, e.g., by using presence of opinion-bearing lexical items (single words or n-grams) to detect subjective sentences, or by exploiting association rule mining for a feature-based analysis of product reviews. These approaches, however, are still far from being able to infer the cognitive and affective information associated with natural language as they mainly rely on knowledge bases that are still too limited to efficiently process text at sentence level. In this thesis, common sense computing techniques are further developed and applied to bridge the semantic gap between word-level natural language data and the concept-level opinions conveyed by these. In particular, the ensemble application of graph mining and multi-dimensionality reduction techniques on two common sense knowledge bases was exploited to develop a novel intelligent engine for open-domain opinion mining and sentiment analysis. The proposed approach, termed sentic computing, performs a clause-level semantic analysis of text, which allows the inference of both the conceptual and emotional information associated with natural language opinions and, hence, a more efficient passage from (unstructured) textual information to (structured) machine-processable data. The engine was tested on three different resources, namely a Twitter hashtag repository, a LiveJournal database and a PatientOpinion dataset, and its performance compared both with results obtained using standard sentiment analysis techniques and using different state-of-the-art knowledge bases such as Princeton’s WordNet, MIT’s ConceptNet and Microsoft’s Probase. Differently from most currently available opinion mining services, the developed engine does not base its analysis on a limited set of affect words and their co-occurrence frequencies, but rather on common sense concepts and the cognitive and affective valence conveyed by these. This allows the engine to be domain-independent and, hence, to be embedded in any opinion mining system for the development of intelligent applications in multiple fields such as Social Web, HCI and e-health. Looking ahead, the combined novel use of different knowledge bases and of common sense reasoning techniques for opinion mining proposed in this work, will, eventually, pave the way for development of more bio-inspired approaches to the design of natural language processing systems capable of handling knowledge, retrieving it when necessary, making analogies and learning from experience

Contextual Social Networking

The thesis centers around the multi-faceted research question of how contexts may be detected and derived that can be used for new context aware Social Networking services and for improving the usefulness of existing Social Networking services, giving rise to the notion of Contextual Social Networking. In a first foundational part, we characterize the closely related fields of Contextual-, Mobile-, and Decentralized Social Networking using different methods and focusing on different detailed aspects. A second part focuses on the question of how short-term and long-term social contexts as especially interesting forms of context for Social Networking may be derived. We focus on NLP based methods for the characterization of social relations as a typical form of long-term social contexts and on Mobile Social Signal Processing methods for deriving short-term social contexts on the basis of geometry of interaction and audio. We furthermore investigate, how personal social agents may combine such social context elements on various levels of abstraction. The third part discusses new and improved context aware Social Networking service concepts. We investigate special forms of awareness services, new forms of social information retrieval, social recommender systems, context aware privacy concepts and services and platforms supporting Open Innovation and creative processes. This version of the thesis does not contain the included publications because of copyrights of the journals etc. Contact in terms of the version with all included publications: Georg Groh, [email protected] zentrale Gegenstand der vorliegenden Arbeit ist die vielschichtige Frage, wie Kontexte detektiert und abgeleitet werden können, die dazu dienen können, neuartige kontextbewusste Social Networking Dienste zu schaffen und bestehende Dienste in ihrem Nutzwert zu verbessern. Die (noch nicht abgeschlossene) erfolgreiche Umsetzung dieses Programmes führt auf ein Konzept, das man als Contextual Social Networking bezeichnen kann. In einem grundlegenden ersten Teil werden die eng zusammenhängenden Gebiete Contextual Social Networking, Mobile Social Networking und Decentralized Social Networking mit verschiedenen Methoden und unter Fokussierung auf verschiedene Detail-Aspekte näher beleuchtet und in Zusammenhang gesetzt. Ein zweiter Teil behandelt die Frage, wie soziale Kurzzeit- und Langzeit-Kontexte als für das Social Networking besonders interessante Formen von Kontext gemessen und abgeleitet werden können. Ein Fokus liegt hierbei auf NLP Methoden zur Charakterisierung sozialer Beziehungen als einer typischen Form von sozialem Langzeit-Kontext. Ein weiterer Schwerpunkt liegt auf Methoden aus dem Mobile Social Signal Processing zur Ableitung sinnvoller sozialer Kurzzeit-Kontexte auf der Basis von Interaktionsgeometrien und Audio-Daten. Es wird ferner untersucht, wie persönliche soziale Agenten Kontext-Elemente verschiedener Abstraktionsgrade miteinander kombinieren können. Der dritte Teil behandelt neuartige und verbesserte Konzepte für kontextbewusste Social Networking Dienste. Es werden spezielle Formen von Awareness Diensten, neue Formen von sozialem Information Retrieval, Konzepte für kontextbewusstes Privacy Management und Dienste und Plattformen zur Unterstützung von Open Innovation und Kreativität untersucht und vorgestellt. Diese Version der Habilitationsschrift enthält die inkludierten Publikationen zurVermeidung von Copyright-Verletzungen auf Seiten der Journals u.a. nicht. Kontakt in Bezug auf die Version mit allen inkludierten Publikationen: Georg Groh, [email protected]

Streaming Infrastructure and Natural Language Modeling with Application to Streaming Big Data

Streaming data are produced in great velocity and diverse variety. The vision of this research is to build an end-to-end system that handles the collection, curation and analysis of streaming data. The streaming data used in this thesis contain both numeric type data and text type data. First, in the field of data collection, we design and evaluate a data delivery framework that handles the real-time nature of streaming data. In this component, we use streaming data in automotive domain since it is suitable for testing and evaluating our data delivery system. Secondly, in the field of data curation, we use a language model to analyze two online automotive forums as an example for streaming text data curation. Last but not least, we present our approach for automated query expansion on Twitter data as an example of streaming social media data analysis. This thesis provides a holistic view of the end-to-end system we have designed, built and analyzed. To study the streaming data in automotive domain, a complex and massive amount of data is being collected from on-board sensors of operational connected vehicles (CVs), infrastructure data sources such as roadway sensors and traffic signals, mobile data sources such as cell phones, social media sources such as Twitter, and news and weather data services. Unfortunately, these data create a bottleneck at data centers for processing and retrievals of collected data, and require the deployment of additional message transfer infrastructure between data producers and consumers to support diverse CV applications. The first part of this dissertation, we present a strategy for creating an efficient and low-latency distributed message delivery system for CV systems using a distributed message delivery platform. This strategy enables large-scale ingestion, curation, and transformation of unstructured data (roadway traffic-related and roadway non-traffic-related data) into labeled and customized topics for a large number of subscribers or consumers, such as CVs, mobile devices, and data centers. We evaluate the performance of this strategy by developing a prototype infrastructure using Apache Kafka, an open source message delivery system, and compared its performance with the latency requirements of CV applications. We present experimental results of the message delivery infrastructure on two different distributed computing testbeds at Clemson University. Experiments were performed to measure the latency of the message delivery system for a variety of testing scenarios. These experiments reveal that measured latencies are less than the U.S. Department of Transportation recommended latency requirements for CV applications, which provides evidence that the system is capable for managing CV related data distribution tasks. Human-generated streaming data are large in volume and noisy in content. Direct acquisition of the full scope of human-generated data is often ineffective. In our research, we try to find an alternative resource to study such data. Common Crawl is a massive multi-petabyte dataset hosted by Amazon. It contains archived HTML web page data from 2008 to date. Common Crawl has been widely used for text mining purposes. Using data extracted from Common Crawl has several advantages over a direct crawl of web data, among which is removing the likelihood of a user\u27s home IP address becoming blacklisted for accessing a given web site too frequently. However, Common Crawl is a data sample, and so questions arise about the quality of Common Crawl as a representative sample of the original data. We perform systematic tests on the similarity of topics estimated from Common Crawl compared to topics estimated from the full data of online forums. Our target is online discussions from a user forum for car enthusiasts, but our research strategy can be applied to other domains and samples to evaluate the representativeness of topic models. We show that topic proportions estimated from Common Crawl are not significantly different than those estimated on the full data. We also show that topics are similar in terms of their word compositions, and not worse than topic similarity estimated under true random sampling, which we simulate through a series of experiments. Our research will be of interest to analysts who wish to use Common Crawl to study topics of interest in user forum data, and analysts applying topic models to other data samples. Twitter data is another example of high-velocity streaming data. We use it as an example to study the query expansion application in streaming social media data analysis. Query expansion is a problem concerned with gathering more relevant documents from a given set that cover a certain topic. Here in this thesis we outline a number of tools for a query expansion system that will allow its user to gather more relevant documents (in this case, tweets from the Twitter social media system), while discriminating from irrelevant documents. These tools include a method for triggering a given query expansion using a Jaccard similarity threshold between keywords, and a query expansion method using archived news reports to create a vector space of novel keywords. As the nature of streaming data, Twitter stream contains emerging events that are constantly changing and therefore not predictable using static queries. Since keywords used in static query method often mismatch the words used in topics around emerging events. To solve this problem, our proposed approach of automated query expansion detects the emerging events in the first place. Then we combine both local analysis and global analysis methods to generate queries for capturing the emerging topics. Experiment results show that by combining the global analysis and local analysis method, our approach can capture the semantic information in the emerging events with high efficiency

The perceptual qualities of concrete : a change in paradigm

La recherche porte sur la perception de qualité des artefacts en béton, et ce depuis la perspective disciplinaire du design industriel. Afin de documenter et examiner les applications et perceptions contemporaines de ce matériau, nous nous attardons à l’évolution des technologies du béton en termes de recettes, techniques de mise en forme, usages, ainsi que ses différentes appréciations. Une revue de littérature a permis de formuler la problématique et d’organiser les données recueillies afin de répondre à nos questions de recherche. Ainsi, nous avons identifié certains événements marquants ayant provoqué des développements importants dans l’évolution du béton. De plus, nous avons regroupé plusieurs témoignages illustrant différentes perceptions du matériau dans des contextes d’usages variés. Les résultats de la recherche ont été interprétés en mettant de l’avant une méthodologie qualitative de recherche. Nous avons également étudié une sélection d’artéfacts en béton à travers des observations empiriques non participatives ainsi que deux cas sélectionnés. Cependant, ce type de recherche à la première personne est influencé par l’auteure, ses expériences vécues, son bagage culturel ainsi que son regard disciplinaire. Ainsi, il était important de valider ces observations teintées par l’appréciation de l’auteure, et ce en triangulant les données avec celles regroupées de documents historiques, scientifiques, techniques et médiatiques. Plusieurs méthodes et outils analytiques ont été mobilisés afin d’organiser les résultats de la recherche. Des cartes chronologiques nous ont permis d’isoler et d’illustrer les étapes déterminantes ayant affecté l’histoire du béton (i.e. la découverte du ciment Portland, etc.). À des catégorisations, nous avons pu classer et comparer certaines données plus spécifiques aux recettes et applications du matériau (i.e. les bétons primitifs – modernes, les bétons structuraux – non-structuraux, etc.). Des cartographiques sémantiques nous ont permis d’interpréter les témoignages compilés des différentes perceptions du béton et ce en se basant sur une échelle sémantique bipolaire (i.e. le béton est laid – beau, le béton est froid – chaud, etc.). Enfin, nous nous sommes basés sur le cadre d’expériences de produits et matériaux (product and material experiences framework) proposé par Desmet et Hekkert (2007) afin d’interpréter les appréciations des artéfacts en béton recueillis à travers la revue de littérature ainsi que les observations empiriques à la première personne. La recherche montre que la perception de qualité du béton fait face à un dualisme qui oppose ses avantages techno-économiques avec son impact environnemental ainsi que la détérioration prématurée de sa surface. Malgré l’appréciation générale de sa versatilité, accessibilité et performance technique, une prise de conscience collective semble rendre les acteurs plus conscients de l’empreinte écologique résultant du cycle de vie du béton. De plus, la recherche démontre que les idéologies sont en train d’évoluer vers des pratiques et modes de vies plus durables malgré les habitudes de surconsommation de la société moderne. En mettant moins l’emphase sur la perfection superficielle, les designers sont de plus en plus motivés à trouver inspiration dans des pratiques plus sensibles et résilientes afin de trouver des solutions durables face aux enjeux urbains. Les dernières tendances révèlent l’émergence d’alternatives plus éco-responsables et innovantes comparées au béton traditionnel. Ainsi, nous trouvons des recettes de béton plus écologiques (i.e. substitution du ciment Portland avec des produits dérivés d’autres industries, etc.) ou des techniques de mise en forme plus optimisées afin de réduire les pertes en offrant un langage esthétique surprenant (i.e. impression 3D, etc.). Ces technologies donnent naissance à de nouvelles applications du béton dans différents domaines inattendus en dehors de l’architecture et de l’ingénierie (i.e. design de produits, art, cinématographie, etc.). La recherche met en lumière changement de paradigme quant à la perception de qualité du béton qui semble être entrainé par la migration des idéologies sociétales vers un modèle qui trouve de la valeur et de la beauté dans les imperfections. Ainsi, des acteurs semblent de plus en plus apprécier le béton avec ses imperfections naturelles, et ont tendance à plus vouloir préserver les artéfacts vieillissants.The research investigates the quality perceptions of concrete artifacts from an industrial design standpoint. In order to document and examine how the material is being used and perceived nowadays, the study looks into the evolution of concrete technologies including its recipes, manufacturing techniques, and uses, as well as its appraisals. A literature review helped us understand the problem field and organize the data amassed in order to find answers to our research questions. We were thus able to identify the critical milestones that triggered change throughout concrete’s historical evolution, as well as gather different testimonies of its perceptions within various contexts. Qualitative research methods were used to interpret our findings. We validated the data based on selected cases as well as non-participatory empirical observations of urban concrete artifacts from a first-person view. This method is influenced by the author’s lived experiences, cultural background, and disciplinary gaze. Therefore, it was necessary to complement the author’s interpretation by triangulating the data retrieved with information gathered from historical, scientific, technical, and mediatic literature. The results were organized and analyzed using various analytical tools and methods. Timeline mappings were used to isolate and illustrate critical milestones triggering change and important developments (e.g. the discovery of Portland Cement, etc.). Categorizations helped us clarify and compare the data gathered to provide a more specific overview of concrete recipes and uses (e.g. primitive – modern concretes, structural – non-structural recipes, etc.). Semantic mappings allowed us to interpret the complied testimonies on how concrete artifacts are perceived in addition to helping us isolate semantic qualities within a bipolar semantic space (e.g. concrete is ugly – beautiful, concrete is cold – warm, etc.). Lastly, a product and material experiences framework (Desmet & Hekkert, 2007) was used to interpret concrete artifacts’ appraisals as found within the testimonies retrieved, in addition to the first-person empirical observations. The research revealed that concrete’s quality perception is facing a dualism which draws attention to its ecological footprint as well as its surface’s premature deterioration with time. Although many seem to appreciate the material’s versatility, accessibility, and structural performance, the dualism can be partially attributed to the evolving collective consciousness which makes actors more aware of concrete’s environmental impacts across its lifecycle. The study thus showed that, despite modern society’s production and consumption habits which focus on the superficial perfection of the material world, ideologies are seen to be evolving and are increasingly interested in more sustainable practices and lifestyles. This can help motivate designers to seek inspiration from emotionally-durable and resilient principles, thus allowing them to better address urban challenges. The latest trends revealed new concrete mixes (e.g. substitution of Portland Cement with by-products of other industries, etc.) and manufacturing techniques (e.g. 3D-printing, etc.) which can offer eco-friendly and innovative alternatives to traditional concrete productions. These emerging solutions are seen to pave the way for unexpected applications in various fields (e.g., product design, art, cinematography, etc.), thus attracting other disciplines beyond engineering and architecture. The changing paradigm in the perception of concrete artifacts shows that value and beauty are not always associated with superficial perfection. In fact, more and more actors are found to reject premature obsolescence by embracing materials’ natural and imperfect behavior as they age with time