Trust and Reputation Modelling for Tourism Recommendations Supported by Crowdsourcing

Tourism crowdsourcing platforms have a profound influence on the tourist behaviour particularly in terms of travel planning. Not only they hold the opinions shared by other tourists concerning tourism resources, but, with the help of recommendation engines, are the pillar of personalised resource recommendation. However, since prospective tourists are unaware of the trustworthiness or reputation of crowd publishers, they are in fact taking a leap of faith when then rely on the crowd wisdom. In this paper, we argue that modelling publisher Trust & Reputation improves the quality of the tourism recommendations supported by crowdsourced information. Therefore, we present a tourism recommendation system which integrates: (i) user profiling using the multi-criteria ratings; (ii) k-Nearest Neighbours (k-NN) prediction of the user ratings; (iii) Trust & Reputation modelling; and (iv) incremental model update, i.e., providing near real-time recommendations. In terms of contributions, this paper provides two different Trust & Reputation approaches: (i) general reputation employing the pairwise trust values using all users; and (ii) neighbour-based reputation employing the pairwise trust values of the common neighbours. The proposed method was experimented using crowdsourced datasets from Expedia and TripAdvisor platforms.info:eu-repo/semantics/publishedVersio

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

Hydrolink 4/2022. Citizen science

Topic: Citizen Scienc

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Monitoring the UK’s wild mammals: A new grammar for citizen science engagement and ecology

Anthropogenic activities have imperilled not just global ecosystems, but also the ecosystem services they provide which are crucial for human livelihoods. To understand these changes, there is a need for effective monitoring over large spatial and temporal scales. This thesis will build on two proposed solutions. First, citizen science – defined here as the involvement of non-professionals in scientific enquiry – allows the crowdsourcing of data collection and classification to expand monitoring in ways that are logistically infeasible for ecologists alone. Second, motion-sensing camera traps can reduce the labour needed for monitoring since they can be deployed for long periods and provide continuous, relatively unbiased observations. In this thesis, I describe MammalWeb, a citizen science project in north-east England where I enlisted the aid of the local community in wild mammal monitoring. Motivated by the current unevenness of survey effort and data for mammals in Great Britain, MammalWeb involves citizen scientists in both the collection and classification of camera trap images, a novel combination. This is a multidisciplinary project, and in the following chapters I will begin, in Chapter 2, with a detailed reflection on the organisation of the MammalWeb citizen science project and approaches to evaluating its performance. I observe that the majority of contributions came from a small subset of citizen scientists. In Chapter 3, I develop an economical approach to deriving consensus classifications from the aggregated input of multiple users, which is a crucial part of many citizen science projects. This is followed in Chapter 4 by a case study of a partnership I initiated between MammalWeb and the local Belmont Community School, where we empowered a group of secondary school students to not only aid in collecting data for MammalWeb, but also design and deliver ecological outreach to their community. This is now the template for a wider network of school partnerships we are pursuing. Chapter 5 will examine common concerns around estimating species occupancy from camera trap data, including post-hoc discretisation of observations and effects of missing data. I also develop a resampling method to account for uncertain detections, a common issue when crowdsourcing data classifications. I show that, through resampling, the estimated parameters from occupancy models are robust against high uncertainty in the underlying detections. Lastly, Chapter 6 will discuss how my work on MammalWeb has laid the foundation for a wider citizen science camera trapping network in the United Kingdom and avenues for future work. Importantly, I show that MammalWeb citizen scientists have been empowered to be more than “mobile sensors” and act as independent researchers who have initiated ecological studies elsewhere

Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges

Widely available digital technologies are empowering citizens who are increasingly well informed and involved in numerous water, climate, and environmental challenges. Citizen science can serve many different purposes, from the "pleasure of doing science" to complementing observations, increasing scientific literacy, and supporting collaborative behaviour to solve specific water management problems. Still, procedures on how to incorporate citizens' knowledge effectively to inform policy and decision-making are lagging behind. Moreover, general conceptual frameworks are unavailable, preventing the widespread uptake of citizen science approaches for more participatory cross-sectorial water governance. In this work, we identify the shared constituents, interfaces, and interlinkages between hydrological sciences and other academic and non-academic disciplines in addressing water issues. Our goal is to conceptualize a transdisciplinary framework for valuing citizen science and advancing the hydrological sciences. Joint efforts between hydrological, computer, and social sciences are envisaged for integrating human sensing and behavioural mechanisms into the framework. Expanding opportunities of online communities complement the fundamental value of on-site surveying and indigenous knowledge. This work is promoted by the Citizens AND HYdrology (CANDHY) Working Group established by the International Association of Hydrological Sciences (IAHS)

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

Citizen science and remote sensing for crop yield gap analysis

The world population is anticipated to be around 9.1 billion in 2050 and the challenge is how to feed this huge number of people without affecting natural ecosystems. Different approaches have been proposed and closing the ‘yield gap’ on currently available agricultural lands is one of them. The concept of ‘yield gap’ is based on production ecological principles and can be estimated as the difference between a benchmark (e.g. climatic potential or water-limited yield) and the actual yield. Yield gap analysis can be performed at different scales: from field to global level. Of particular importance is estimating the yield gap and revealing the underlying explanatory factors contributing to it. As decisions are made by farmers, farm level yield gap analysis specifically contributes to better understanding, and provides entry points to increased production levels in specific farming systems. A major challenge for this type of analysis is the high data standards required which typically refer to (a) large sample size, (b) fine resolution and (c) great level of detail. Clearly, obtaining information about biophysical characteristics and crop and farm management for individual agricultural activities within a farm, as well as farm and farmer’s characteristics and socio-economic conditions for a large number of farms is costly and time-consuming. Nowadays, the proliferation of different types of mobile phones (e.g., smartphones) equipped with sensors (e.g., GPS, camera) makes it possible to implement effective and low-cost “bottom-up” data collection approaches such as citizen science. Using these innovative methodologies facilitate the collection of relatively large amounts of information directly from local communities. Moreover, other data collection methods such as remote sensing can provide data (e.g., on actual crop yield) for yield gap analysis. The main objective of this thesis, therefore, was to investigate the applicability of innovative data collection approaches such as crowdsourcing and remote sensing to support the assessment and monitoring of crop yield gaps. To address the main objective, the following research questions were formulated: 1) What are the main factors causing the yield gaps at the global, regional and crop level? 2) How could data for yield gap explaining factors be collected with innovative “bottom-up” approaches? 3) What are motivations of farmers to participate in agricultural citizen science? 4) What determines smallholder farmers to use technologies (e.g., mobile SMS) for agricultural data collection? 5) How can synergy of crowdsourced data and remote sensing improve the estimation and explanation of yield variability? Chapter 2 assesses data availability and data collection approaches for yield gap analysis and provides a summary of yield gap explaining factors at the global, regional and crop level, identified by previous studies. For this purpose, a review of yield gap studies (50 agronomic-based peer-reviewed articles) was performed to identify the most commonly considered and explaining factors of the yield gap. Using the review, we show that management and edaphic factors are more often considered to explain the yield gap compared to farm(er) characteristics and socio-economic factors. However, when considered, both farm(er) characteristics and socio-economic factors often explain the yield gap. Furthermore, within group comparison shows that fertilization and soil fertility factors are the most often considered management and edaphic groups. In the fertilization group, factors related to quantity (e.g., N fertilizer quantity) are more often considered compared to factors related to timing (e.g., N fertilizer timing). However, when considered, timing explained the yield gap more often. Finally, from the results at regional and crop level, it was evident that the relevance of factors depends on the location and crop, and that generalizations should not be made. Although the data included in yield gap analysis also depends on the objective, knowledge of explaining factors, and methods applied, data availability is a major limiting factor. Therefore, bottom-up data collection approaches (e.g., crowdsourcing) involving agricultural communities can provide alternatives to overcome this limitation and improve yield gap analysis. Chapter 3 explores the motivations of farmers to participate in citizen science. Building on motivational factors identified from previous citizen science studies, a questionnaire based methodology was developed which allowed the analysis of motivational factors and their relation to farmers’ characteristics. Using the developed questionnaire, semi-structured interviews were conducted with smallholder farmers in three countries (Ethiopia, Honduras and India). The results show that for Indian farmers a collectivistic type of motivation (i.e., contribute to scientific research) was more important than egoistic and altruistic motivations. For Ethiopian and Honduran farmers an egoistic intrinsic type of motivation (i.e., interest in sharing information) was most important. Moreover, the majority of the farmers in the three countries indicated that they would like to receive agronomic advice, capacity building and seed innovation as the main returns from the citizen science process. Country and education level were the two most important farmers’ characteristics that explained around 20% of the variation in farmers’ motivations. The results also show that motivations to participate in citizen science are different for smallholders in agriculture compared to other sectors. For example fun has appeared to be an important egoistic intrinsic factor to participate in other citizen science projects, the smallholder farmers involved in this research valued ‘passing free time’ the lowest. Chapter 4 investigates the factors that determine farmers to adopt mobile technology for agricultural data collection. To identify the factors, the unified theory of acceptance and use of technology (UTAUT2) model was employed and extended with additional constructs of trust, mastery-approach goals and personal innovativeness in information technology. As part of the research, we setup data collection platforms using open source applications (Frontline SMS and Ushahidi) and farmers provided their farm related information using SMS for two growing seasons. The sample for this research consisted of group of farmers involved in a mobile SMS experiment (n=110) and another group of farmers which was not involved in a mobile SMS experiment (n=110), in three regions of Ethiopia. The results from the structural equation modelling showed that performance expectancy, effort expectancy, price value and trust were the main factors that influence farmers to adopt mobile SMS technology for agricultural data collection. Among these factors, trust is the strongest predictor of farmer’s intention to adopt mobile SMS. This clearly indicates that in order to use the citizen science approach in the agricultural domain, establishing a trusted relationship with the smallholder farming community is crucial. Given that performance expectancy significantly predicted farmer’s behavioural intention to adopt mobile SMS, managers of agricultural citizen science projects need to ensure that using mobile SMS for agricultural data collection offers utilitarian benefits to the farmers. The importance of effort expectancy on farmer’s intention to adopt mobile SMS clearly indicates that mobile phone software developers need to develop easy to use mobile applications. Chapter 5 demonstrates the results of synergetic use of remote sensing and crowdsourcing for estimating and explaining crop yields at the field level. Sesame production on medium and large farms in Ethiopia was used as a case study. To evaluate the added value of the crowdsourcing approach to improve the prediction of sesame yield using remote sensing, two independent models based on the relationship between vegetation indices (VIs) and farmers reported yield were developed and compared. The first model was based on VI values extracted from all available remote sensing imagery acquired during the optimum growing period (hereafter optimum growing period VI). The second model was based on VI values extracted from remote sensing imagery acquired after sowing and before harvest dates per field (hereafter phenologically adjusted VI). To select the images acquired between sowing and harvesting dates per field, farmers crowdsourced crop phenology information was used. Results showed that vegetation indices derived based on farmers crowdsourced crop phenology information had a stronger relationship with sesame yield compared to vegetation indices derived based on the optimum growing period. This implies that using crowdsourced information related to crop phenology per field used to adjust the VIs, improved the performance of the model to predict sesame yield. Crowdsourcing was further used to identify the factors causing the yield variability within a field. According to the perception of farmers, overall soil fertility was the most important factor explaining the yield variability within a field, followed by high presence of weeds. Chapter 6 discusses the main findings of this thesis. It draws conclusions about the main research findings in each of the research questions addressed in the four main chapters. Finally, it discusses the necessary additional steps (e.g., data quality, sustainability) in a broader context that need to be considered to utilize the full potential of innovative data collection approaches for agricultural citizen science.</p