A Crowdsourcing Based Framework for Sentiment Analysis: A Product Reputation

As social networking has spread, people started sharing their personal opinions and thoughts widely via these online platforms. The resulting vast valuable data represent a rich source for companies to deduct their products’ reputation from both social media and crowds’ judgments. To exploit this wealth of data, a framework was proposed to collect opinions and rating scores respectively from social media and crowdsourcing platform to perform sentiment analysis, provide insights about a product and give consumers’ tendencies. During the analysis process, a consumer category (strict) is excluded from the process of reaching a majority consensus. To overcome this, a fuzzy clustering is used to compute consumers’ credibility. The key novelty of our approach is the new layer of validity check using a crowdsourcing component that ensures that the results obtained from social media are supported by opinions extracted directly from real-life consumers. Finally, experiments are carried out to validate this model (Twitter and Facebook were used as data sources). The obtained results show that this approach is more efficient and accurate than existing solutions thanks to our two-layer validity check design

Perspectives in machine learning for wildlife conservation

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation

Geoinformatics in Citizen Science

The book features contributions that report original research in the theoretical, technological, and social aspects of geoinformation methods, as applied to supporting citizen science. Specifically, the book focuses on the technological aspects of the field and their application toward the recruitment of volunteers and the collection, management, and analysis of geotagged information to support volunteer involvement in scientific projects. Internationally renowned research groups share research in three areas: First, the key methods of geoinformatics within citizen science initiatives to support scientists in discovering new knowledge in specific application domains or in performing relevant activities, such as reliable geodata filtering, management, analysis, synthesis, sharing, and visualization; second, the critical aspects of citizen science initiatives that call for emerging or novel approaches of geoinformatics to acquire and handle geoinformation; and third, novel geoinformatics research that could serve in support of citizen science

Making digital history: The impact of digitality on public participation and scholarly practices in historical research

This thesis investigates tow key questions: firstly, how do two broad groups - academic, family and local historians, and the public - evaluate, use, and contribute to digital history resources? And consequently, what impact have digital technologies had on public participation and scholarly practices in historical research? Analysing the impact of design on participant experiences and the reception of digital historiography by demonstrating the value of methods drawn from human-computer interaction, including heuristic evaluation, trace ethnography and semi-structured interviews. This thesis also investigates the relationship between heritage crowdsourcing projects (which ask the public to help with meaningful, inherently rewarding tasks that contribute to a shared, significant goal or research interest related to cultural heritage collections or knowledge) and the development of historical skills and interests. It situates crowdsourcing and citizen history within the broader field of participatory digital history and then focuses on the impact of digitality on the research practices of faculty and community historians. Chapter 1 provides an overview of over 400 digital history projects aimed at engaging the public or collecting, creating or enhancing records about historical materials for scholarly and general audiences. Chapter 2 discusses design factors that may influence the success of crowdsourcing projects. Following this, Chapter 3 explores the ways in which some crowdsourcing projects encourage deeper engagement with history or science, and the role of communities of practice in citizen history. Chapter 4 shifts our focus from public participation to scholarly practices in historical research, presenting the results of interviews conducted with 29 faculty and community historians. Finally, the Conclusion draws together the threads that link public participation and scholarly practices, teasing out the ways in which the practices of discovering, gathering, creating and sharing historical materials and knowledge have been affected by digital methods, tools and resources