Arno Scharl

Incrementally computed information landscapes are an effective means to visualize longitudinal changes in large document repositories. Resembling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Addressing the growing number of documents to be processed by state-of-the-art knowledge discovery applications, we introduce an incremental, scalable approach for generating such landscapes. The processing pipeline includes a number of sequential tasks, from crawling, filtering and pre-processing Web content to projecting, labeling and rendering the aggregated information. Incremental processing steps are localized in the projection stage consisting of document clustering, cluster force-directed placement and fast document positioning. We evaluate the proposed framework by contrasting layout qualities of incremental versus non-incremental versions. Documents for the experiments stem from the blog sample of the Media Watch on Climate Change (www.ecoresearch.net/climate). Experimental results indicate that our incremental computation approach is capable of accurately generating dynamic information landscapes.

The Media Watch on Climate Change is a domain-specific news aggregation portal that combines a portfolio of semantic services with a visual information exploration and retrieval interface (www.ecoresearch.net/climate). It provides a comprehensive and continuously updated account of online media coverage on climate change and related issues. The portal aggregates, filters and visualizes environmental content from the Web sites of AngloAmerican news media sites, blogs, environmental organizations, and the Fortune 1000 companies. The heterogeneous sources reflect the perceptions of different stakeholders, who have a unique stance on environmental strategies and a varied means to promote and implement them. The system builds contextualized information spaces by enriching a content repository with geospatial, semantic and temporal annotations, and by applying semi-automated ontology learning to create a controlled vocabulary for structuring the stored information. Portlets visualize the different dimensions of the contextualized information spaces, providing the user with multiple views on the latest news media coverage. Context information facilitates access to complex datasets and helps users navigate large repositories of Web documents. Currently, the system synchronizes information landscapes, domain ontologies, geographic maps, tag clouds and just-in-time information retrieval agents that suggest similar topics and nearby locations. Acknowledgement. RAVEN (Relation Analysis and Visualization for Evolving Networks) is a research project within the strategic objective FIT-IT Semantic Systems (www.modul.ac.at/nmt/raven). The system been developed by S. Kamran Ali Ahmad (knowledge planet), A. Dickinger (usability), A. Hubmann-Haidvogel (frontend), H.-P. Lang (ontology map), W. Rafelsberger (tag cloud), A. Scharl (project lead), H. Stern (geotagging), A. Weichselbraun (technical lead), G. Wohlgenannt (system architecture), and D. Zibold (ontology map). The semantic map's force-directed placement algorithms have jointly been developed with V. Sabol and M. Muhr from Know-Center Graz.

The complexity of the social, political and economical settings in which tourism enterprises operate, increasingly require them to perform data analytics tasks that rely on data from various domains (e.g., economy, environmental sustainability). A survey of tourism practitioners performed in this study showed that although such cross-domain analytics are important, they are primarily performed by relying on manual data collection and aggregation, which is both time-consuming and error-prone. This paper investigates the suitability of Linked Data technologies to support data aggregation tasks needed for establishing such complex analytics systems. To that end, a prototypical implementation is developed that relies on Linked Data as a technological platform for integrating data from three major tourism data sources: TourMIS, World Bank and Eurostat. Enabled by this integrated data, the ETIHQ Dashboard for data analytics was implemented, the first visual data analytics system that supports cross-domain analytics over tourism, economic and sustainability indicators. An exploratory evaluation performed with practitioners shows that this Linked Data enabled system could potentially bring important improvements in terms of execution times and answer quality when compared to current manual approaches typically used by tourism practitioners in daily practice.

This paper introduces an interactive news flow visualization that reveals emerging topics in dynamic digital content archives. The presented approach combines several visual metaphors and can be easily adapted to present multi-source social media datasets. In the context of this work, we discuss various methods for improving visual interfaces for accessing aggregated media representations. We combine falling blocks with bar graphs and arcs, but keep these elements clearly separated in different areas of the display. The arc metaphor is adapted and enriched with interactive controls to help users understand the dataset's underlining meaning. The paper describes the implementation of the prototype and discusses design issues with a particular emphasis on visual metaphors to highlight hidden relations in digital content. We conclude with a summary of the lessons learnt and the integration of the visualization component into the Media Watch on Climate Change (www.ecoresearch.net/climate), a public Web portal that aggregates environmental information from a variety of online sources including news media, blogs and other social media such as Twitter, YouTube and Facebook.

Sentiment detection automatically identifies emotions in textual data. The increasing amount of emotive documents available in corporate databases and on the World Wide Web calls for automated methods to process this important source of knowledge. Sentiment detection draws attention from researchers and practitioners alike - to enrich business intelligence applications, for example, or to measure the impact of customer reviews on purchasing decisions. Most sentiment detection approaches do not consider language ambiguity, despite the fact that one and the same sentiment term might differ in polarity depending on the context, in which a statement is made. To address this shortcoming, this paper introduces a novel method that uses Naive Bayes to identify ambiguous terms. A contextualized sentiment lexicon stores the polarity of these terms, together with a set of co-occurring context terms. A formal evaluation of the assigned polarities confirms that considering the usage context of ambiguous terms improves the accuracy of high-throughput sentiment detection methods. Such methods are a prerequisite for using sentiment as a metadata element in storage and distributed file-level intelligence applications, as well as in enterprise portals that provide a semantic repository of an organization's information assets.

Mechanised labour and games with a purpose are the two most popular human computation genres, frequently employed to support research activities in fields as diverse as natural language processing, semantic web or databases. Research projects typically rely on either one or the other of these genres, and therefore there is a general lack of understanding of how these two genres compare and whether and how they could be used together to offset their respective weaknesses. This paper addresses these open questions. It first identifies the differences between the two genres, primarily in terms of cost, speed and result quality, based on existing studies in the literature. Secondly, it reports on a comparative study which involves performing the same task through both genres and comparing the results. The study's findings demonstrate that the two genres are highly complementary, which not only makes them suitable for different types of projects, but also opens new opportunities for building cross-genre human computation solutions that exploit the strengths of both genres simultaneously.

This paper presents a method for integrating DBpedia data into an ontology learning system that automatically suggests labels for relations in domain ontologies based on large corpora of unstructured text. The method extracts and aggregates verb vectors for semantic relations identified in the corpus. It composes a knowledge base which consists of (i) centroids for known relations between domain concepts, (ii) mappings between concept pairs and the types of known relations, and (iii) ontological knowledge retrieved from DBpedia.Refining similarities between the verb centroids of labeled and unlabeled relations by means of including domain and range constraints applying DBpedia data yields relation type suggestions. A formal evaluation compares the accuracy and average ranking performance of this hybrid method with the performance of methods that solely rely on corpus data and those that are only based on reasoning and external data sources.

The simplicity of using Web 2.0 platforms and services has resulted in an abundance of user-generated content. A significant part of this content contains user opinions with clear economic relevance - customer and travel reviews, for example, or the articles of well-known and respected bloggers who influence purchase decisions. Analyzing and acting upon user-generated content is becoming
imperative for marketers and social scientists who aim to gather feedback from very large user communities. Sentiment detection, as part of opinion mining, supports these efforts by identifying and aggregating polar opinions - i.e., positive or negative statements about facts.
For achieving accurate results, sentiment detection requires a correct interpretation of language, which remains a challenging task due to the inherent ambiguities of human languages. Particular attention has to be directed to the context of opinionated terms when trying to resolve these ambiguities. Contextualized sentiment lexicons address
this need by considering the sentiment term's context in their evaluation but are usually limited to one domain, as many contextualizations are not stable across domains. This paper introduces a method which identifies unstable contextualizations and refines the contextualized sentiment dictionaries accordingly, eliminating the need for specific training data for each individual domain. An extensive evaluation compares the accuracy of this approach with results obtained from domain-specific corpora.

Abstract This paper presents a novel method for contextualizing and enriching large semantic knowledge bases for opinion mining with a focus on Web intelligence platforms and other high-throughput big data applications. The method is not only applicable to traditional sentiment lexicons, but also to more comprehensive, multi-dimensional affective resources such as SenticNet. It comprises the following steps: (i) identify ambiguous sentiment terms, (ii) provide context information extracted from a domain-specific training corpus, and (iii) ground this contextual information to structured background knowledge sources such as ConceptNet and WordNet. A quantitative evaluation shows a significant improvement when using an enriched version of SenticNet for polarity classification. Crowdsourced gold standard data in conjunction with a qualitative evaluation sheds light on the strengths and weaknesses of the concept grounding, and on the quality of the enrichment process.

This chapter presents the Media Watch on Climate Change, a publicly available Web intelligence portal that collects, aggregates and visualizes large archives of digital content from multiple stakeholder groups (documents and user comments from news media, blogs, user-generated content from Facebook, Twitter and YouTube, corporate and NGO Web sites, and a range of other sources). An visual dashboard with trend charts and complex map projections not only shows how often and where environmental information is published, but also provides a real-time account of concepts that stakeholders associate with climate change. Positive or negative sentiment is computed automatically, which sheds light on the impact of education and public outreach campaigns that target environmental literacy, and helps to gain a better understanding of how others perceive climate-related issues.

Visual techniques provide an intuitive way of making sense of the
large amounts of microposts available from social media sources,
particularly in the case of emerging topics of interest to a global
audience, which often raise controversy among key stakeholders.
Micropost streams are context-dependent and highly dynamic in nature. We describe a visual analytics platform to handle highvolume micropost streams from multiple social media channels. For each post we extract key contextual features such as location, topic and sentiment, and subsequently render the resulting multidimensional information space using a suite of coordinated views
that support a variety of complex information seeking behaviors. We also describe three new visualization techniques that extend the original platform to account for the dynamic nature of micropost streams through dynamic topography information landscapes, news flow diagrams and longitudinal cross-media analyses.

This paper presents the US Election 2004 Web Monitor, a public Web portal that captured trends in political media coverage before and after the 2004 US Presidential Election. Developed by the authors of this article, the webLyzard suite of Web mining tools provided the required functionality to aggregate and analyze about half a million documents in weekly intervals (www.webLyzard.com). The study paid particular attention to the editorial slant, defined as the quantity and tone of a Web site’s coverage as influenced by its editorial position. The observable attention and attitude towards the candidates served as proxies of editorial slant. The system identified attention by determining the frequency of candidate references and measured attitude towards the candidate by looking for positive and negative expressions that co-occur with these references. Keywords and perceptual maps summarized the most important topics associated with the candidates, placing special emphasis on environmental issues.

The webLyzard media monitoring and Web intelligence platform (www.webLyzard.com) presented in this paper is a generic tool for assessing the strategic positioning of an organization and the effectiveness of its communication strategies. The platform captures and aggregates large archives of digital content from multiple stakeholder groups. Each week it processes millions of documents and user comments from news media, blogs, Web 2.0 platforms such as Facebook, Twitter and YouTube, the Web sites of companies and NGOs, and other sources. An interactive dashboard with trend charts and complex map projections shows how often and where information is published. It also provides a real-time account of topics that stakeholders associate with an organization. Positive or negative sentiment is computed automatically, which reflects the impact of public relations and marketing campaigns.

Destination image significantly influences a tourist's decision-making process. The impact of news media coverage on destination image has attracted research attention and became particularly evident after catastrophic events such as the 2004 Indian Ocean earthquake that triggered a series of lethal tsunamis. Building upon previous research, this paper analyzes the prevalence of tourism destinations among 162 international media sites. Term frequency captures the attention a destination receives­ from a general and, after contextual filtering, from a tourism perspective. Calculating sentiment estimates positive and negative media influences on destination image at a given point in time. Identifying semantic associations with the names of countries and major cities, the results of co-occurrence analysis reveal the public profiles of destinations, and the impact of current events on media coverage. These results allow national tourism organizations to assess how their destination is covered by news media in general, and in a specific tourism context. To guide analysts and marketers in this assessment, an iterative analysis of semantic associations through natural language processing extracts tourism knowledge automatically, and represents this knowledge as ontological structures.

Policy makers and environmental organizations have a keen interest in awareness building and the evolution of stakeholder opinions on environmental issues. Mere polarity detection, as provided by many existing methods, does not suffice to understand the emergence of collective awareness. Methods for extracting affective knowledge should be able to pinpoint opinion targets within a thread. Opinion target extraction provides a more accurate and fine-grained identification of opinions expressed in online media. This paper compares two different approaches for identifying potential opinion targets and applies them to comments from the YouTube video sharing platform. The first approach is based on statistical keyword analysis in conjunction with sentiment classification on the sentence level. The second approach uses dependency parsing to pinpoint the target of an opinionated term. A case study based on YouTube postings applies the developed methods and measures their ability to handle noisy input data from social media streams.

Mobile marketing, also known as wireless marketing, promises vast opportunities. Still in an experimental phase, businesses have little experience using this new marketing tool. Mobile services offer companies powerful marketing potential via direct communication with consumers, anytime and anywhere, but little research on this subject exists. This paper discusses Short Message Services (SMS), which belong to the first and most successful forms of mobile data transmission. Based on a literature review and exploratory qualitative research, this paper defines mobile marketing, describes its most popular application, text messaging, introduces a conceptual model of success factors for implementing mobile marketing, and proposes future research avenues.

This study investigates the coverage of solid waste management on 1142 websites maintained by companies, news media and non-governmental organizations to validate an automated approach to content and language analysis. First, a frequency analysis of waste management terms sheds light on the breadth and depth of their environmental discourses, revealing that corporate and media attention to waste management is small compared with that of non-governmental organizations. Second, an investigation of their attitudes toward waste management suggests that companies avoid negative information in environmental communication, unlike news media or non-governmental organizations. Ultimately, an automated tool for ontology building is employed to gain insights into companies' shared understanding of waste management. The ontology obtained indicates that companies conceptualize waste management as a business process rather than framing it from an ecological perspective, which is in line with findings from previous research. Copyright © 2006 John Wiley & Sons, Ltd and ERP Environment.

Games with a purpose are an increasingly popular mechanism for leveraging the wisdom of the crowds to address tasks which are trivial for humans but still not solvable by computer algorithms in a satisfying manner. As a novel mechanism for structuring human-computer interactions, a key challenge when creating them is motivating users to participate while generating useful and unbiased results. This paper focuses on important design choices and success factors of effective games with a purpose. Our findings are based on lessons learned while developing and deploying Sentiment Quiz, a crowdsourcing application for creating sentiment lexicons (an essential component of most sentiment detection algorithms). We describe the goals and structure of the game, the underlying application framework, the sentiment lexicons gathered through crowdsourcing, as well as a novel approach to automatically extend the lexicons by means of a bootstrapping process. Such an automated extension further increases the efficiency of the acquisition process by limiting the number of terms that need to be gathered from the game participants.

The complexity of the socio-, political- and economical settings in which tourism enterprises operate, increasingly require them to make decisions that take into account data from various domains (e.g., economy, environmental sustainability). Based on a practitioners' survey that we performed, we conclude that although such cross-domain decisions are important, they are primarily performed by relying on manual data collection and aggregation, which is both time-consuming and error-prone. We propose a solution that relies on Linked Data as a technological platform for integrating data from three major tourism data sources: TourMIS, World Bank and Eurostat. Enabled by this integrated data, we developed the ETIHQ Dashboard, the first visual decision support system that supports cross-domain decisions over tourism, economic and sustainability indicators. An evaluation performed with practitioners shows that this Linked Data enabled systems brings important improvements in terms of execution times (28% faster) and answer quality when compared to current manual approaches.

Extracting and analyzing affective knowledge from social media in a structured manner is a challenging task. Decision makers require insights into the public perception of a company's products and services, as a strategic feedback channel to guide communication campaigns, and as an early warning system to quickly react in the case of unforeseen events. The approach presented in this paper goes beyond bipolar metrics of sentiment. It combines factual and affective knowledge extracted from rich public knowledge bases to analyze emotions expressed towards specific entities (targets) in social media. We obtain common and common-sense domain knowledge from DBpedia and ConceptNet to identify potential sentiment targets. We employ affective knowledge about emotional categories available from SenticNet to assess how those targets and their aspects (e.g. specific product features) are perceived in social media. An evaluation shows the usefulness and correctness of the extracted domain knowledge, which is used in a proof-of-concept data analytics application to investigate the perception of car brands on social media in the period between September and November 2015.

Recent research shows the potential of utilizing data collected through Web 2.0 applications to capture changes in a domain's terminology. This paper presents an approach to augment corpus-based ontology learning by considering terms from collaborative tagging systems, social networking platforms, and micro-blogging services. The proposed framework collects information on the domain's terminology from domain documents and a seed ontology in a triple store. Data from social sources such as Delicious, Flickr, Technorati and Twitter provide an outside view of the domain and help incorporate external knowledge into the ontology learning process. The neural network technique of spreading activation is used to identify relevant new concepts, and to determine their positions in the extended ontology. Evaluating the method with two measures (PMI and expert judgements) demonstrates the significant benefits of social evidence sources for ontology learning.

Emerging information technologies play an increasingly important role, not only to automate tasks within organizations but also to provide the infrastructure to facilitate communication across organizational boundaries, to implement one-to-one marketing strategies, or to manage business relationships. Web Information Systems (WIS) provide a platform that can help establish and manage customer relationships in ways that were not feasible with traditional business models and architectures. They facilitate the delivery of customized content to end consumers, reflecting their unique needs and individual preferences. In order to establish electronic commerce as a new business paradigm, corresponding changes in information technology, organizational structure, and the corporate value chain are critical. This paper proposes a conceptual model to support the task of balancing flexibility needs with the specific requirements of electronic transactions.

Als wesentliche Erkenntnis- und Gestaltungsziele dieses Buches sind die Propagierung von Referenzmodellen zurbranchenspezifischen Beschreibung von Masseninformationssystemen idealtypischer Unternehmen, der Abbau inner- undzwischenbetrieblicher Kommunikationsbarrieren sowie die drastische Reduktion der mit dem Entwurf vonImplementierungsmodellen verbundenen Kosten zu nennen. Zur graphischen Repräsentation des Referenzmodells wurde eindokumentorientiertes Metamodell, die erweiterte World Wide Web-Design-Technik (eW3DT), entwickelt. Dieses beruht aufeiner komparativen Analyse existierender Verfahren zur systemischen Visualisierung von Informationssystemen und wird inder Folge herangezogen, um die Daten- und Navigationsmodelle der funktionalen Teilbereiche desMasseninformationssystems eines idealtypischen Unternehmens der Branche Informationstechnik ausführlich zu beschreiben.

Linking named entities to structured knowledge sources paves the way for state-of-the-art Web intelligence applications which assign sentiment to the correct entities, identify trends, and reveal relations between organizations, persons and products. For this purpose this paper introduces Recognyze, a named entity linking component that uses background knowledge obtained from linked data repositories, and outlines the process of transforming heterogeneous data silos within an organization into a linked enterprise data repository which draws upon popular linked open data vocabularies to foster interoperability with public data sets. The presented examples use comprehensive real-world data sets from Orell Füssli Business Information, Switzerland's largest business information provider. The linked data repository created from these data sets comprises more than nine million triples on companies, the companies' contact information, key people, products and brands. We identify the major challenges of tapping into such sources for named entity linking, and describe required data pre-processing techniques to use and integrate such data sets, with a special focus on disambiguation and ranking algorithms. Finally, we conduct a comprehensive evaluation based on business news from the New Journal of Zurich and AWP Financial News to illustrate how these techniques improve the performance of the Recognyze named entity linking component.

Understanding dynamic changes in tourist perceptions and analyzing user-generated content to assess the impact of campaigns and promotional activities are among the key questions facing many destination management organizations. Web intelligence platforms help to answer these questions, particularly when they are scalable enough to analyze and visualize Web-scale information flows in real time. This paper presents a tourism Web intelligence platform for acquiring and processing real-time streams of online content from Web sites and social media platforms, advanced methods to extract factual and affective knowledge from these sources, and interactive visualization techniques to explore topical trends and assess the impact of communication campaigns. The extracted knowledge is analyzed from a destination image perspective, incorporating Aaker’s dimensions of brand personality. The chapter highlights the importance of real-time analytics solutions for marketers to respond in a timely manner and adapt their positioning strategies.

Collaborative ontology building requires innovative navigational instruments that improve content exploration and the creation of shared meaning. Building upon an existing architecture for automated ontology learning from unstructured textual resources developed by the authors, this paper presents a Web-based user interaction framework encompassing three major components: (i) real-time visualizations of ontology evolution with time interval and confidence sliders, (ii) traditional ontology editing environments, and (iii) multi-player online games leveraging social networking platforms in the tradition of games with a purpose. A prototype in the environmental domain will showcase the integration of powerful search capabilities with novel graphbased interfaces for guiding novice and expert users alike.

This paper presents a technique for the visual analysis of topical shifts in dynamically changing textual archives. Our approach is based on the well-known information landscape metaphor, whereby topical changes are represented by changes in landscape topography. Incremental clustering and multi-dimensional scaling algorithms are periodically applied to a changing document set for generating a series of information landscapes. The resulting landscapes are suitable for dynamic Web interfaces, enabling the user to explore topical relationships and understand topical shifts and trends in changing document repositories.

This paper presents the technology base and roadmap of the Climate Change Collaboratory, a Web-based platform that aims to strengthen the relations between scientists, educators, environmental NGOs, policy makers, news media and corporations - stakeholders who recognize the need for adaptation and mitigation, but differ in world-views, goals and agendas. The collaboratory manages expert knowledge and provides a platform for effective communication and collaboration. It aims to assist networking with leading international organizations, bridges the science-policy gap and promotes rich, self-sustaining community interaction to translate knowledge into coordinated action. Innovative survey instruments in the tradition of ”games with a purpose” will create shared meaning through collaborative ontology building and leverage social networking platforms to capture indicators of environmental attitudes, lifestyles and behaviors.

This paper discusses the role of context information in building the next generation of human-centered information systems, and classifies the various aspects of contextualization with a special emphasis on the production and consumption of digital content. The real-time annotation of resources is a crucial element when moving from content aggregators (which process third-party digital content) to context-aware visual authoring environments (which allow users to create and edit their own documents). We present a publicly available prototype of such an environment, which required a major redesign of an existing Web intelligence and media monitoring framework to provide real-time data services and synchronize the text editor with the frontend’s visual components. The paper concludes with a summary of achieved results and an outlook on possible future research avenues including multi-user support and the visualization of document evolution.

Recent research shows the potential of utilizing data collected through Web 2.0 applications to capture domain evolution. Relying on external data sources, however, often introduces delays due to the time spent retrieving data from these sources. The method introduced in this paper streamlines the data acquisition process by applying optimal stopping theory. An extensive evaluation demonstrates how such an optimization improves the processing speed of an ontology refinement component which uses Delicious to refine ontologies constructed from unstructured textual data while having no significant impact on the quality of the refinement process. Domain experts compare the results retrieved from optimal stopping with data obtained from standardized techniques to assess the effect of optimal stopping on data quality and the created domain ontology.

In a global and interconnected economy, decision makers often need to consider information from various domains. A tourism destination manager, for example, has to correlate tourist behavior with financial and environmental indicators to allocate funds for strategic long-term investments. Statistical data underpins a broad range of such cross-domain decision tasks. A variety of statistical datasets are available as Linked Open Data, often incorporated into visual analytics solutions to support decision making. What are the principles, architectures, workflows and implementation design patterns that should be followed for building such visual cross-domain decision support systems. This article introduces a methodology to integrate and visualize cross-domain statistical data sources by applying selected RDF Data Cube (QB) principles. A visual dashboard built according to this methodology is presented and evaluated in the context of two use cases in the tourism and telecommunications domains.

The identification and labelling of non-hierarchical relations are among the most challenging tasks in ontology learning. This paper describes a bottom-up approach for automatically suggesting ontology link types. The presented method extracts verb-vectors from semantic relations identified in the domain corpus, aggregates them by computing centroids for known relation types, and stores the centroids in a central knowledge base. Comparing verb-vectors extracted from unknown relations with the stored centroids yields link type suggestions. Domain experts evaluate these suggestions, refining the knowledge base and constantly improving the component's accuracy. A final evaluation provides a detailed statistical analysis of the introduced approach.

Online news media coverage regarding a destination, a form of big data, can affect destination image and influence the number of tourist arrivals. Sentiment analysis extracts the valence of an author’s perception about a topic by rating a segment of text as either positive or negative. The sentiment of online news media can be viewed as a leading indicator for actual tourism demand. The aim of this study is to examine if web sentiment of online news media coverage of four European cities (Berlin, Brussels, Paris, and Vienna) possesses information to predict actual tourist arrivals. This study is the first to use web sentiment for forecasting tourism demand. Automated semantic routines were conducted to analyze the sentiment of online news media coverage. Due to the differing data frequencies of tourist arrivals (monthly) and web sentiment indicators (daily), the MIxed-DAta Sampling (MIDAS) modeling approach was applied. Results indicate that MIDAS models including various web sentiment indicators outperform time-series and naïve benchmarks in terms of typical accuracy measures. This study shows that utilizing online news media coverage as an indication of destination image can improve tourism demand forecasting. Because destination image is dynamic, the results can vary depending on time period of the analysis and the destination. A managerial implication of the forecast evaluation exercise is that destination management organizations (DMOs) should add models incorporating web sentiment data to their forecast modeling toolkit to further improve the accuracy of their tourism demand forecasts.

Analysts are often interested in how sentiment towards an organization, a product or a particular technology changes over time. Popular methods that process unstructured textual material to automatically detect sentiment based on tagged dictionaries are not capable of fulfilling this task, even when coupled with part-of speech tagging, a standard component of most text processing toolkits that distinguishes grammatical categories such as article, noun, verb, and adverb. Small corpus size, ambiguity and subtle incremental change of tonal expressions between different versions of a document complicate sentiment detection. Parsing grammatical structures, by contrast, outperforms dictionary-based approaches in terms of reliability, but usually suffers from poor scalability due to its computational complexity. This work provides an over view of different dictionary- and machine-learning-based sentiment detection methods and evaluates them on several Web corpora.After identifying the shortcomings of these methods, the paper proposes an approach based on automatically building Tagged Linguistic Unit (TLU) databases to overcome the restrictions of dictionaries with a limited set of tagged tokens.

The World Wide Web as a social network reflects changes of interest in certain domains. It has been shown that free online content available through blogs, wikis, news media and online forums is a valuable source of information to identify trends in certain domains. Utilizing this data, one can construct ontologies that describe this information and provide a semantically correct overview of a domain. Tracked over time this also enables a user to identify trends and hypes. The decentralised structure of the Internet, the huge amount of data and upcoming Web2.0 technologies pose several challenges to a crawling system for ontology learning, evolution and trend analysis. This paper presents a distributed crawling system with browser integration for Web2.0. The proposed crawler is a high performance Web data retrieval system aimed to gather browser-equivalent textual Web content and prepare it for ontology learning.

General Purpose Technologies, or GPTs are defined in the economic literature as the key technologies that shape the economy. Despite the large conceptual literature base on Blockchain potential to revolutionize the current economic system, there is a lack of empirical research on its economic nature and the course of technological development. The paper at hand covers this research gap by providing the quantitative approach aimed at understanding the evolutionary path of Blockchain and its scope for improvement – an acknowledged feature of a GPT - in line with the industrial dynamics and GPT literature. The longitudinal analysis of Blockchain-related patents from PATSTAT and their rule-based classification both from technological and application perspectives is complemented by the study of Blockchain media landscape to provide insights into the social context in which it emerges. The increasing amount of patents addressing essential technical issues, such as security, scalability, and usability contribute to wider adoption of the technology, whereas the positive sentiment in the media associated with Blockchain creates beneficial social context for its development. The empirical results advance the claim that Blockchain does show a positive scope for improvement peculiar to the GPTs in the making and, therefore, deserves attention as a technology that will define macroeconomic dynamics in a long term.

By providing interoperability and shared meaning across actors and domains, lightweight domain ontologies are a cornerstone technology of the Semantic Web. This chapter investigates evidence sources for ontology learning and describes a generic and extensible approach to ontology learning that combines such evidence sources to extract domain concepts, identify relations between the ontology’s concepts, and detect relation labels automatically. An implementation illustrates the presented ontology learning and relation labeling framework and serves as the basis for dis- cussing possible pitfalls in ontology learning. Afterwards, three use cases demonstrate the usefulness of the presented framework and its application to real-world problems.

Although ontologies are central to the Semantic Web, current ontology learning methods primarily make use of a single evidence source and are agnostic in their internal representations to the evolution of ontology knowledge. This article presents a continuous ontology learning framework that overcomes these shortcomings by integrating evidence from multiple, heterogeneous sources (unstructured, structured, social) in a consistent model, and by providing mechanisms for the fine-grained tracing of the evolution of domain ontologies. The presented framework supports a tight integration of human and machine computation. Crowdsourcing in the tradition of games with a purpose performs the evaluation of the learned ontologies and facilitates the automatic optimization of learning algorithms.

Novel social media collaboration platforms, such as games with a purpose and mechanised labour marketplaces, are increasingly used for enlisting large populations of non-experts in crowdsourced knowledge acquisition processes. Climate Quiz uses this paradigm for acquiring environmental domain knowledge from non-experts. The game's usage statistics and the quality of the produced data show that Climate Quiz has managed to attract a large number of players but noisy input data and task complexity led to low player engagement and suboptimal task throughput and data quality. To address these limitations, the authors propose embedding the game into a hybrid-genre workflow, which supplements the game with a set of tasks outsourced to micro-workers, thus leveraging the complementary nature of games with a purpose and mechanised labour platforms. Experimental evaluations suggest that such workflows are feasible and have positive effects on the game's enjoyment level and the quality of its output.

Dynamic topography information landscapes are capable of visualizing longitudinal changes in large document repositories. Resembling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Acknowledging the growing number of documents to be processed by state-of-the-art Web intelligence applications, we present a scalable, incremental approach for generating such landscapes. The processing pipeline includes a number of sequential tasks, from crawling, filtering and pre-processing Web content to projecting, labeling and rendering the aggregated information. Processing steps central to incremental processing are found in the projection stage which consists of document clustering, cluster force-directed placement, and fast document positioning. We introduce two different positioning methods and compare them in an incremental setting using two different quality measures. The evaluation is performed on a set of approximately 5000 documents taken from the environmental blog sample of the Media Watch on Climate Change (www.ecoresearch.net/climate), a Web content aggregator about climate change and related environmental issues that serves static versions of the information landscapes presented in this paper as part of a multiple coordinated view representation.

This paper presents a method to automatically mirror, process, and compare large samples of text corpora from Web-based information systems. The wealth of textual information contained in publicly available Web sites is converted into aggregated representations through textual analysis. The application of word lists, keyword analysis, term clustering, and correspondence analyses to identify and represent semantic relationships, including their longitudinal patterns, is illustrated through a case study that investigates the global coverage of solar power technologies in international media. The resulting graphs, indicators and tables describe complex relationships and developments that are hard to capture in traditional ways. As such they facilitate investigations about the nature and dynamics of Web content.

This paper presents a Web intelligence portal that captures and aggregates news and social media coverage about “Game of Thrones”, an American drama television series created for the HBO television network based on George R.R. Martin’s series of fantasy novels. The system collects content from the Web sites of Anglo-American news media as well as from four social media platforms: Twitter, Facebook, Google+ and YouTube. An interactive dashboard with trend charts and synchronized visual analytics components not only shows how often Game of Thrones events and characters are being mentioned by journalists and viewers, but also provides a real-time account of concepts that are being associated with the unfolding storyline and each new episode. Positive or negative sentiment is computed automatically, which sheds light on the perception of actors and new plot elements.

Understanding stakeholder perceptions and assessing the impact of campaigns are key questions of communication experts. Web intelligence platforms help to answer such questions, provided that they are scalable enough to analyze and visualize information flows from volatile online sources in real time. This paper presents a distributed architecture for aggregating Web content repositories from Web sites and social media streams, memory-efficient methods to extract factual and affective knowledge, and interactive visualization techniques to explore the extracted knowledge. The presented examples stem from the Media Watch on Climate Change, a public Web portal that aggregates environmental content from a range of online sources.

This paper presents the IDIOM Media Watch on Climate Change (www.ecoresearch.net/climate), a prototypical implementation of an environmental portal that emphasizes the importance of location data for advanced Web applications. The introductory section outlines the process of retrofitting existing knowledge repositories with geographical context information, a process also referred to as geotagging. The paper then describes the portal's functionality, which aggregates, annotates and visualizes environmental articles from 150 Anglo-American news media sites. From 300,000 news media articles gathered in weekly intervals, the system selects about 10,000 focusing on environmental issues. The crawled data is indexed and stored in a central repository. Geographic location represents a central aspect of the application, but not the only dimension used to organize and filter content. Applying the concepts of location and topography to semantic similarity, the paper concludes with discussing information landscapes as alternative interface metaphor for accessing large Web repositories.

Dynamic environments require effective update mechanisms for ontologies to incorporate new knowledge. In this position paper we present a dynamic framework for ontology learning which integrates automated learning methods with rapid user feedback mechanism to build and extend lightweight domain ontologies at regular intervals. Automated methods collect evidence from a variety of heterogeneous sources and generate an ontology with spreading activation techniques, while crowdsourcing in the form of Games with a Purpose validates the new ontology elements. Special data structures support dynamic confidence management in regards to three major aspects of the ontology: (i) the incoming facts collected from evidence sources, (ii) the relations that constitute the extended ontology, and (iii) the observed quality of evidence sources. Based on these data structures we propose trend detection experiments to measure not only significant changes in the domain, but also in the conceptualization suggested by user feedback

In the evolution of mankind, markets have developed as independent and highly functional institutions. This paper identifies the major structures, processes, and players of real markets on an abstract scale, and transforms these elements into virtual objects for the purpose of designing a conceptual model of an electronic market. The particular form of market it concentrates on, the Oriental bazaar, is the traditional and dominant market type in Oriental cities. The main characteristic of an Oriental bazaar is the negotiation system that determines the prices of individual products and services.

The advantages and positive effects of multiple coordinated views on search performance have been documented in several studies. This paper describes the implementation of multiple coordinated views within the Media Watch on Climate Change, a domain-specific news aggregation portal available at
www.ecoresearch.net/climate that combines a portfolio of semantic services with a visual information exploration and retrieval interface. The system builds contextualized information spaces by enriching the content repository with geospatial, semantic and temporal annotations, and by applying semi-automated ontology learning to create a controlled vocabulary for structuring the stored information. Portlets visualize the different dimensions of the contextualized information spaces, providing the user with multiple views on the latest news media coverage. Context information facilitates access to complex datasets and helps users navigate large repositories of Web documents. Currently, the system synchronizes information landscapes, domain ontologies, geographic maps, tag clouds and just-in-time information retrieval agents that suggest similar topics and nearby locations.

Mobile marketing offers direct communication with consumers, anytime and anyplace. This paper reviews mobile marketing and then investigates the most successful form of mobile communication, short message services (SMS), via a quantitative content analysis of the Fortune Global 500 Web sites and qualitative interviews with European experts. The content analysis explores the diffusion of SMS technology and sheds light on mobile marketing campaigns of large multinational organizations. Combining a literature review with results from the qualitative survey leads to a conceptual model of successful SMS advertising. The paper closes with future research avenues for this emerging marke ting tool.

The online games market has matured in recent years. It is now a
multi-billion dollar business with hundreds of millions players
worldwide. At the same time, social networking platforms have
witnessed unprecedented growth rates and increasingly offer developer
interfaces to leverage and extend their built-in core functionality.
Benefiting from these trends, games with a purpose are
a proven way of leveraging the wisdom of the crowds to address
tasks that are trivial for humans but still not solvable by computer
algorithms in a satisfying manner. This paper presents an application
framework to develop interactive games with a purpose on
top of social networking platforms, suitable for deployment in
both mobile and Web-based environments. A set of analytic tools
helps to evaluate the results and to pre-process the gathered data
for use in external applications.

Digital ecosystems typically involve a large number of participants from different sectors who generate rapidly growing archives of unstructured text. Measuring the frequency of certain terms to determine the popularity of a topic is comparably straightforward. Detecting sentiment expressed in user-generated electronic content is more challenging, especially in the case of digital ecosystems comprising heterogeneous sets of multilingual documents. This paper describes the use of language-specific grammar patterns and multilingual tagged dictionaries to detect sentiment in German and English document repositories. Digital ecosystems may contain millions of frequently updated documents, requiring sentiment detection methods that maximize throughput. The ideal combination of high-throughput techniques and more accurate (but slower) approaches depends on the specific requirements of an application. To accommodate a wide range of possible applications, this paper presents (i) an adaptive method, balancing accuracy and scalability of multilingual textual sources, (ii) a generic approach for generating language- specific grammar patterns and multilingual tagged dictionaries, and (iii) an extensive evaluation verifying the method's performance based on Amazon product reviews and user evaluations from Sentiment Quiz, a “game with a purpose” that invites users of the Facebook social networking platform to assess the sentiment of individual sentences.

Given the intense attention that environmental topics such as climate change attract in news and social media coverage, scientists and communication professionals want to know how different stakeholders perceive observable threats and policy options, how specific media channels react to new insights, and how journalists present scientific knowledge to the public. This paper investigates the potential of semantic technologies to address these questions. After summarizing methods to extract and disambiguate context information, we present visualization techniques to explore the lexical, geospatial, and relational context of topics and entities referenced in these repositories. The examples stem from the Media Watch on Climate Change, the Climate Resilience Toolkit and the NOAA Media Watch—three applications that aggregate environmental resources from a wide range of online sources. These systems not only show the value of providing comprehensive information to the public, but also have helped to develop a novel communication success metric that goes beyond bipolar assessments of sentiment.

Organizations require tools that can assess their online reputations as well as the impact of their marketing and public outreach activities. The Media Watch on Climate Change is a Web intelligence and online collaboration platform that addresses this requirement. It aggregates large archives of digital content from multiple stakeholder groups and enables the co-creation and visualization of evolving knowledge archives. Here, the authors introduce the base platform and a context-aware document editor as an add-on that supports concurrent authoring by multiple users. While documents are being edited, semantic methods analyze them on the fly to recommend related content. The system computes positive or negative sentiment automatically to provide a better understanding of third-party perceptions. The editor is part of an interactive dashboard that uses trend charts and map projections to show how often and where relevant information is published, and to provide a real-time account of concepts that stakeholders associate with a topic.

Innovation substantially reduces the practical value of traditional communication models. This paper examines the role of conceptual, user-centric modelling of web information systems as a primary means of standardized visual communication between and within organizations. It presents the development and potential of the extended World Wide Web Design Technique as a visual, consistent, and semantically rich language to share knowledge about content and structure of both planned and deployed systems (“Explanation”). As web information systems represent semantic networks in themselves, it is only natural to leverage their semantics to provide analytical tools and intuitive user interfaces. Visual frameworks based on the extended World Wide Web Design Technique enable interactive visualization of the users' access patterns. Limited, statistically oriented representations of commercially available web-tracking software are enhanced by a map-like view based on the system's unique topology. When integrated into the user interface via multiple, tightly coupled views, such automatically generated site maps help users to explore the available navigation space (“Exploration”).

With more than 800 million monthly active users, Facebook bears significant potential for science projects. Climate Quiz is an interactive Web application in the tradition of "games with a purpose" that leverages this potential to create metadata through a crowdsourcing-based approach. It presents participants with two types of challenges: (1) selecting the correct relation to connect two environmental concepts, and (2) answering climate-related multiple choice questions. Climate Quiz aims to create shared meaning through collaborative ontology building, a process that captures emergent semantics and elicits formal knowledge in the form of a domain model. As an innovative survey instrument, the application leverages social networking platforms to capture indicators of environmental attitudes, lifestyles and behaviors.

Background. Sentic computing relies on welldefined affective models of different complexity - polarity to distinguish positive and negative sentiment,
for example, or more nuanced models to capture expressions of human emotions. When used to measure
communication success, even the most granular affective model combined with sophisticated machine learning approaches may not fully capture an organisation’s
strategic positioning goals. Such goals often deviate from
the assumptions of standardised affective models. While
certain emotions such as Joy and Trust typically represent desirable brand associations, specific communication goals formulated by marketing professionals often
go beyond such standard dimensions. For instance, the
brand manager of a television show may consider fear
or sadness to be desired emotions for its audience.
Method. This article introduces expansion techniques
for affective models, combining common and commonsense knowledge available in knowledge graphs with
language models and affective reasoning, improving coverage and consistency as well as supporting domainspecific interpretations of emotions.
Results and Conclusions. An extensive evaluation
compares the performance of different expansion techniques: (i) a quantitative evaluation based on the revisited Hourglass of Emotions model to assess perfor
mance on complex models that cover multiple affective categories, using manually compiled gold standard
data, and (ii) a qualitative evaluation of a domainspecific affective model for television programme brands.
The results of these evaluations demonstrate that the
introduced techniques support a variety of embeddings
and pre-trained models. The paper concludes with a
discussion on applying this approach to other scenarios
where affective model resources are scarce.

This paper presents a method to integrate external knowledge sources such as DBpedia and OpenCyc into an ontology learning system that automatically suggests labels for unknown relations in domain ontologies based on large corpora of unstructured text. The method extracts and aggregates verb vectors from semantic relations identified in the corpus. It composes a knowledge base which consists of (i) verb centroids for known relations between domain concepts, (ii) mappings between concept pairs and the types of known relations, and (iii) ontological knowledge retrieved from external sources. Applying semantic inference and validation to this knowledge base improves the quality of suggested relation labels. A formal evaluation compares the accuracy and average ranking precision of this hybrid method with the performance of methods that solely rely on corpus data and those that are only based on reasoning and external data sources.

Although the field has led to promising early results, the use of crowdsourcing as an integral part of science projects is still regarded with skepticism by some, largely due to a lack of awareness of the opportunities and implications of utilizing these new techniques. We address this lack of awareness, firstly by highlighting the positive impacts that crowdsourcing has had on Natural Language Processing research. Secondly, we discuss the challenges of more complex methodologies, quality control, and the necessity to deal with ethical issues. We conclude with future trends and opportunities of crowdsourcing for science, including its potential for disseminating results, making science more accessible, and enriching educational programs.

A context-aware approach based on machine learning and lexical analysis identifies ambiguous terms and stores them in contextualized sentiment lexicons, which ground the terms to concepts corresponding to their polarity.

The perceived lack of connection between global environmental problems and an individual’s immediate context is among the main reasons why people prove resistant to changing their decisions and actions towards a more sustainable way of life. By bridging this gap and better relating individual behavior to its local and global consequences, properly designed eco-feedback technology may evoke intrinsic and extrinsic motivations and may help to translate awareness into collective action.

The concept of culture encompasses the way people relate to the environment and to technology. It influences the perception of control mechanism and guides individual and collective behavior. Considering cultural aspects when designing eco-feedback technology, thus, may improve its persuasive force. This paper presents a conceptual analysis of relevant cultural aspects of the Brazilian society that impact eco-feedback technology adoption and appropriation, and discusses new forms of communication and collaboration that support these processes.