Capturing perceived everyday lived landscapes through gamification and active crowdsourcing

Summary Landscapes are distinguishable areas of the earth with distinct characters comprised of tangible and intangible dimensions and entities. Interactions between humans and landscapes influence social, physical and mental well-being as well as guide behaviour. Understanding how landscapes are perceived has thus gained traction in sustainable and inclusive policy and decision making processes and public participation is called for. The recognised importance of understanding landscapes from an experiential and perceptual perspective and incorporating public participation in data generation efforts is reflected in overarching conventions, policy guidelines and frameworks including the European Landscape Convention (ELC), the Millennium Ecosystem Assessment (MEA), Natures Contributions to People (NCP) and the Landscape Character Assessment (LCA) framework. Major challenges for these conventions and frameworks are 1) how to collect data on landscape experiences and perceptions from a diverse group of individuals, 2) how to integrate and link physical entities, sensory experiences and intangible dimensions of landscapes and 3) how to identify other potential sources of landscape relevant information. The abundance of storage space and the accessibility of broadband internet have led to a burgeoning of user generated natural language content. In parallel, various paradigms of exploiting ubiquitous internet access for research purposes have emerged, including crowdsourcing, citizen science, volunteered geographic information and public participation geographic information systems. These low cost approaches have shown great potential in generating large amounts of data, however, they struggle with motivating and retaining participants. Gamification - broadly defined as adding entertaining or playful elements to applications or processes - has been found to increase user motivation and has explicitly been called for in landscape perception and preference research to diversify participant demographics. Meanwhile, natural language has been found to be deeply intertwined with thought and emotion and has been identified as a rich source of semantic data on how landscapes are perceived and experienced. Written texts and the ways in which these can be analysed have gained particular interest. Therefore, the overall goal of this thesis is to develop and implement a gamified crowdsourcing application to collect natural language landscape descriptions and to analyse and explore the contributions in terms of how landscapes are perceived through sensory experiences and how additional landscape relevant natural language can be identified. To approach this goal, I first elicit key data and feature requirements to collect landscape relevant information from a heterogeneous audience. Guided by the identified requirements, I develop and implement Window Expeditions, a gamified active crowdsourcing platform geared towards collecting natural language descriptions of everyday lived landscapes. The generated corpus of natural language is explored using computational methods and I present and discuss the results in light of who the contributors are, the locations from which participants contribute and salient terms found in English and German. In a further step I annotate a subset of English contributions according to the contained biophysical elements, sensory experiences and cultural ecosystem (dis)services and explore these in terms of how they are linked. Finally, I present a novel approach of using a curated high quality landscape specific dataset to computationally identify similar documents in other corpora using sentence-transformers. Using the Mechanics, Dynamics and Aesthetics (MDA) framework, the aesthetics of discovery, expression and fellowship were identified as most fitting for an active crowdsourcing platform. In addition, four groups of main dynamics were found, namely general dynamics of user interactions, contribution dynamics, exploration dynamics and moderation dynamics. The application was gamified by introducing points and leader boards and the platform was implemented in German and English (with French being added at a later point) to collect landscape descriptions in multiple languages. Demographic information was collected about the users including their year of birth, their gender, if they were at home whilst contributing and what languages users believed to be fluent in. Using the Mechanics, Dynamics and Aesthetics (MDA) framework, the aesthetics of discovery, expression and fellowship were identified as most fitting for an active crowdsourcing platform. In addition, four groups of main dynamics were found, namely general dynamics of user interactions, contribution dynamics, exploration dynamics and moderation dynamics. The application was gamified by introducing points and leader boards and the platform was implemented in German and English (with French being added at a later point) to collect landscape descriptions in multiple languages. Demographic information was collected about the users including their year of birth, their gender, if they were at home whilst contributing and what languages users believed to be fluent in reporting not being at home (n = 172) who were more likely to contribute from areas of herbaceous vegetation. Terms describing salient elements of everyday lived environments such as "tree", "house", "garden" and "street", as well as weather related phenomena and colours were found frequently in both English and German contributions in the generated corpus. Further, terms related to space, time and people were found significantly more frequently in the generated corpus compared to general natural language and representative landscape image descriptions highlighting the importance of spatial features as well as people and the times at which these were observed. Notably, descriptions referring to trees and birds were frequently found in the contributed texts, underlining their saliency in everyday lived landscapes. The results show biophyiscal terms related to vegetation (n = 556) and the built environment (n = 468) as well as weather related terms (n = 452) to be most prominent. Further, contributions referencing visual (n = 186) and auditory (n = 96) sensory experiences were found most often with positive sensory experiences being most common (n = 168) followed by neutral (n = 86) and negative (n = 68). In regards to the intangible dimensions captured in the contributed landscape descriptions, recreation (n = 68) was found most often followed by heritage (n = 36), identity (n = 26) and tranquillity (n = 23). Through linking biophysical elements, sensory experiences and cultural ecosystem (dis)services, the results show that the biophysical category of animals appears often with the sensory experience of smell/taste and the biophysical category of moving objects appears more than expected with the sensory experience of sound. Further, the results show the cultural ecosystem service of inspiration to often appear with the biophysical category of natural features and tranquillity with weather. Using a curated subcorpus of English natural language landscape descriptions (n = 428) collected with Window Expeditions, similar documents in other collections were identified. Through translating documents to vectors by means of sentence-transformers and calculating cosine similarity scores, a total of 6075 to 8172 documents were identified to be similar to contributions to Window Expeditions, depending on if the initial dataset was prefiltered for biophysical noun lemmas (a list of biophysical landscape elements derived from the Window Expeditions corpus) and Craik’s list adjectives (a list of common adjectives used to describe landscapes). Latent Dirichlet allocation topic modelling, a clustering approach which is commonly used to identify overarching topics or themes in collections of natural language, shows four distinct clusters in both Window Expeditions as well as in the corpus of identified similar documents, namely urban and residential, rural and natural, autumn and colours and snow and weather. Overall, the results presented in this thesis provide further evidence to work that natural language is a rich source of landscape specific information, capturing underlying semantics of a multitude of referenced landscape dimensions. In particular, this thesis demonstrates that computationally aided approaches to analysing and exploring landscape relevant textual data can give detailed insights into salient features of landscapes and how individuals perceive and experience these. Especially when complemented by human annotation, natural language landscape descriptions are a welcome source of data about a landscape’s biophysical elements, individual sensory experiences in landscapes and the perceived cultural ecosystem (dis)services. The findings of this thesis are accompanied by various limitations, chief amongst which are the possibilities of users to falsify their locations, the rather small amount of data that was collected through Window Expeditions and the Eurocentric definitions and approaches common in landscape perception research. The former two limitations can be addressed through implementational reiterations and promotional efforts, whereas the latter limitation calls for further consideration of the socio-culturally induced construction of landscape perception research and a rethinking of holistic approaches, especially in multicultural participatory contexts. The work presented in this thesis shows great potential in complementing landscape perception research with gamified methods of data generation. Active crowdsourcing can be a cost efficient and scalable approach of generating much needed data from a diverse audience. Exploring landscape relevant natural language with both quantitative and qualitative methods from various disciplines including geographic information science, linguistics and machine learning can lead to new insights into landscape perception, sensory landscape experiences and how these are expressed

Research opportunities for argumentation in social networks

Nowadays, many websites allow social networking between their users in an explicit or implicit way. In this work, we show how argumentation schemes theory can provide a valuable help to formalize and structure on-line discussions and user opinions in decision support and business oriented websites that held social networks between their users. Two real case studies are studied and analysed. Then, guidelines to enhance social decision support and recommendations with argumentation are provided.This work summarises results of the authors joint research, funded by an STMS of the Agreement Technologies COST Action 0801, by the Spanish government grants [CONSOLIDER-INGENIO 2010 CSD2007-00022, and TIN2012-36586-C03-01] and by the GVA project [PROMETEO 2008/051].Heras Barberá, SM.; Atkinson, KM.; Botti Navarro, VJ.; Grasso, F.; Julian Inglada, VJ.; Mcburney, PJ. (2013). Research opportunities for argumentation in social networks. 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Non-heurisitc Machine Learning Apprach for Classifying Twitter Content

In online social networks like Twitter, the users usually get inundated with the continuous stream of short messages or tweets. This problem can be handled using classification. Classification is a supervised data mining technique which involves assigning a label to a set of unlabeled objects. A conventional approach for classifying text or tweets is to extract features from the linguistic content posted by the users. A recurrent problem in classification is feature selection, that is, to decide the best set of features for making a particular classification decision among the infinite possible different sets of features. This process usually involves heuristic approaches that require manual feature selection by experts, which involves guesswork, prior information about the dataset and a great deal of tweaking and experimental validation. To address this problem we propose and employ a non-heuristic machine learning approach which will automatically decide the feature set for a classification task. Our analysis shows that our automated feature selection process for Twitter content classification performs on par with current state-of-the-art approaches which incorporate painstaking, time-consuming human effort to manually and heuristically select a feature set. This approach will improve the timeliness and accessibility of data mining social media data streams.Computer Scienc

Automated motif identification: Analysing Flickr images to identify popular viewpoints in Europe’s protected areas

Visiting landscapes and appreciating them from specific viewpoints is not a new phenomenon. Such so-called motifs were popularised by travel guides and art in the romantic era, and find their contemporary digital twins through images captured in social media. We developed and implemented a conceptual model of motifs, based around spatial clustering, image similarity and the appreciation of a motif by multiple individuals. We identified 119 motifs across Europe, using 2146176 georeferenced Creative Commons Flickr images found in Natura 2000 protected areas. About 65% of motifs contain cultural elements such as castles or bridges. The remaining 35% are natural features, and biased towards coastal elements such as cliffs. Characterisation and localisation of motifs could allow identification of locations subject to increased pressure, and thus disturbance, especially since the visual characteristics of motifs allow managers to explore why sites are being visited. Future work will include methods of calculating image similarity using tags, explore different algorithms for assessing content similarity and study the behaviour of motifs through time

Conference Programme v1 & Book of Abstracts

7th INTERNATIONAL CONFERENCE ON MEANING AND KNOWLEDGE REPRESENTATION MKR2018 @ ITB Dublin 4, 5 and 6 July, 201

A survey on cost-effective context-aware distribution of social data streams over energy-efficient data centres

Social media have emerged in the last decade as a viable and ubiquitous means of communication. The ease of user content generation within these platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges, including derivation of real-time meaningful insights from effectively gathered social information, as well as a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general. In this article we present a comprehensive survey that outlines the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centres supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. We systematize the existing literature and proceed to identify and analyse the main research points and industrial efforts in the area as far as modelling, simulation and performance evaluation are concerned