About Challenges in Data Analytics and Machine Learning for Social Good

The large number of new services and applications and, in general, all our everyday activities resolve in data mass production: all these data can become a golden source of information that might be used to improve our lives, wellness and working days. (Interpretable) Machine Learning approaches, the use of which is increasingly ubiquitous in various settings, are definitely one of the most effective tools for retrieving and obtaining essential information from data. However, many challenges arise in order to effectively exploit them. In this paper, we analyze key scenarios in which large amounts of data and machine learning techniques can be used for social good: social network analytics for enhancing cultural heritage dissemination; game analytics to foster Computational Thinking in education; medical analytics to improve the quality of life of the elderly and reduce health care expenses; exploration of work datafication potential in improving the management of human resources (HRM). For the first two of the previously mentioned scenarios, we present new results related to previously published research, framing these results in a more general discussion over challenges arising when adopting machine learning techniques for social good

On Designing a Time Sensitive Interaction Graph to Identify Twitter Opinion Leaders

What happened on social media during the recent pandemic? Who was the opinion leader of the conversations? Who influenced whom? Were they medical doctors, ordinary people, scientific experts? Did health institutions play an important role in informing and updating citizens? Identifying opinion leaders within social platforms is of particular importance and, in this paper, we introduce the idea of a time sensitive interaction graph to identify opinion leaders within Twitter conversations. To evaluate our proposal, we focused on all the tweets posted on Twitter in the period 2020-21 and we considered just the ones that were Italian-written and were related to COVID-19. After mapping these tweets into the graph, we applied the PageRank algorithm to extract the opinion leaders of these conversations. Results show that our approach is effective in identifying opinion leaders and therefore it might be used to monitor the role that specific accounts (i.e., health authorities, politicians, city administrators) have within specific conversations

Development of Context-Aware Recommenders of Sequences of Touristic Activities

En els últims anys, els sistemes de recomanació s'han fet omnipresents a la xarxa. Molts serveis web, inclosa la transmissió de pel·lícules, la cerca web i el comerç electrònic, utilitzen sistemes de recomanació per facilitar la presa de decisions. El turisme és una indústria molt representada a la xarxa. Hi ha diversos serveis web (e.g. TripAdvisor, Yelp) que es beneficien de la integració de sistemes recomanadors per ajudar els turistes a explorar destinacions turístiques. Això ha augmentat la investigació centrada en la millora dels recomanadors turístics per resoldre els principals problemes als quals s'enfronten. Aquesta tesi proposa nous algorismes per a sistemes recomanadors turístics que aprenen les preferències dels turistes a partir dels seus missatges a les xarxes socials per suggerir una seqüència d'activitats turístiques que s'ajustin a diversos contextes i incloguin activitats afins. Per aconseguir-ho, proposem mètodes per identificar els turistes a partir de les seves publicacions a Twitter, identificant les activitats experimentades en aquestes publicacions i perfilant turistes similars en funció dels seus interessos, informació contextual i períodes d'activitat. Aleshores, els perfils d'usuari es combinen amb un algorisme de mineria de regles d'associació per capturar relacions implícites entre els punts d'interès de cada perfil. Finalment, es fa un rànquing de regles i un procés de selecció d'un conjunt d'activitats recomanables. Es va avaluar la precisió de les recomanacions i l'efecte del perfil d'usuari. A més, ordenem el conjunt d'activitats mitjançant un algorisme multi-objectiu per enriquir l'experiència turística. També realitzem una segona fase d'anàlisi dels fluxos turístics a les destinacions que és beneficiós per a les organitzacions de gestió de destinacions, que volen entendre la mobilitat turística. En general, els mètodes i algorismes proposats en aquesta tesi es mostren útils en diversos aspectes dels sistemes de recomanació turística.En los últimos años, los sistemas de recomendación se han vuelto omnipresentes en la web. Muchos servicios web, incluida la transmisión de películas, la búsqueda en la web y el comercio electrónico, utilizan sistemas de recomendación para ayudar a la toma de decisiones. El turismo es una industria altament representada en la web. Hay varios servicios web (e.g. TripAdvisor, Yelp) que se benefician de la inclusión de sistemas recomendadores para ayudar a los turistas a explorar destinos turísticos. Esto ha aumentado la investigación centrada en mejorar los recomendadores turísticos y resolver los principales problemas a los que se enfrentan. Esta tesis propone nuevos algoritmos para sistemas recomendadores turísticos que aprenden las preferencias de los turistas a partir de sus mensajes en redes sociales para sugerir una secuencia de actividades turísticas que se alinean con diversos contextos e incluyen actividades afines. Para lograr esto, proponemos métodos para identificar a los turistas a partir de sus publicaciones en Twitter, identificar las actividades experimentadas en estas publicaciones y perfilar turistas similares en función de sus intereses, contexto información y periodos de actividad. Luego, los perfiles de usuario se combinan con un algoritmo de minería de reglas de asociación para capturar relaciones entre los puntos de interés que aparecen en cada perfil. Finalmente, un proceso de clasificación de reglas y selección de actividades produce un conjunto de actividades recomendables. Se evaluó la precisión de las recomendaciones y el efecto de la elaboración de perfiles de usuario. Ordenamos además el conjunto de actividades utilizando un algoritmo multi-objetivo para enriquecer la experiencia turística. También llevamos a cabo un análisis de los flujos turísticos en los destinos, lo que es beneficioso para las organizaciones de gestión de destinos, que buscan entender la movilidad turística. En general, los métodos y algoritmos propuestos en esta tesis se muestran útiles en varios aspectos de los sistemas de recomendación turística.In recent years, recommender systems have become ubiquitous on the web. Many web services, including movie streaming, web search and e-commerce, use recommender systems to aid human decision-making. Tourism is one industry that is highly represented on the web. There are several web services (e.g. TripAdvisor, Yelp) that benefit from integrating recommender systems to aid tourists in exploring tourism destinations. This has increased research focused on improving tourism recommender systems and solving the main issues they face. This thesis proposes new algorithms for tourism recommender systems that learn tourist preferences from their social media data to suggest a sequence of touristic activities that align with various contexts and include affine activities. To accomplish this, we propose methods for identifying tourists from their frequent Twitter posts, identifying the activities experienced in these posts, and profiling similar tourists based on their interests, contextual information, and activity periods. User profiles are then combined with an association rule mining algorithm for capturing implicit relationships between points of interest apparent in each profile. Finally, a rule ranking and activity selection process produces a set of recommendable activities. The recommendations were evaluated for accuracy and the effect of user profiling. We further order the set of activities using a multi-objective algorithm to enrich the tourist experience. We also carry out a second-stage analysis of tourist flows at destinations which is beneficial to destination management organisations seeking to understand tourist mobility. Overall, the methods and algorithms proposed in this thesis are shown to be useful in various aspects of tourism recommender systems

Social media mining as an opportunistic citizen science model in ecological monitoring: a case study using invasive alien species in forest ecosystems.

Dramatische ökologische, ökonomische und soziale Veränderungen bedrohen die Stabilität von Ökosystemen weltweit und stellen zusammen mit neuen Ansprüchen an die vielfältigen Ökosystemdienstleistungen von Wäldern neue Herausforderungen für das forstliche Management und Monitoring dar. Neue Risiken und Gefahren, wie zum Beispiel eingebürgerte invasive Arten (Neobiota), werfen grundsätzliche Fragen hinsichtlich etablierter forstlicher Managementstrategien auf, da diese Strategien auf der Annahme stabiler Ökosysteme basieren. Anpassungsfähige Management- und Monitoringstrategien sind deshalb notwendig, um diese neuen Bedrohungen und Veränderungen frühzeitig zu erkennen. Dies erfordert jedoch ein großflächiges und umfassendes Monitoring, was unter Maßgabe begrenzter Ressourcen nur bedingt möglich ist. Angesichts dieser Herausforderungen haben Forstpraktiker und Wissenschaftler begonnen auch auf die Unterstützung von Freiwilligen in Form sogenannter „Citizen Science“-Projekte (Bürgerwissenschaft) zurückzugreifen, um zusätzliche Informationen zu sammeln und flexibel auf spezifische Fragestellungen reagieren zu können. Mit der allgemeinen Verfügbarkeit des Internets und mobiler Geräte ist in Form sogenannter sozialer Medien zudem eine neue digitale Informationsquelle entstanden. Mittels dieser Technologien übernehmen Nutzer prinzipiell die Funktion von Umweltsensoren und erzeugen indirekt ein ungeheures Volumen allgemein zugänglicher Umgebungs- und Umweltinformationen. Die automatische Analyse von sozialen Medien wie Facebook, Twitter, Wikis oder Blogs, leistet inzwischen wichtige Beiträge zu Bereichen wie dem Monitoring von Infektionskrankheiten, Katastrophenschutz oder der Erkennung von Erdbeben. Anwendungen mit einem ökologischen Bezug existieren jedoch nur vereinzelt, und eine methodische Bearbeitung dieses Anwendungsbereichs fand bisher nicht statt. Unter Anwendung des Mikroblogging-Dienstes Twitter und des Beispiels eingebürgerter invasiver Arten in Waldökosystemen, verfolgt die vorliegende Arbeit eine solche methodische Bearbeitung und Bewertung sozialer Medien im Monitoring von Wäldern. Die automatische Analyse sozialer Medien wird dabei als opportunistisches „Citizen Science“-Modell betrachtet und die verfügbaren Daten, Aktivitäten und Teilnehmer einer vergleichenden Analyse mit existierenden bewusst geplanten „Citizen Science“-Projekten im Umweltmonitoring unterzogen. Die vorliegenden Ergebnisse zeigen, dass Twitter eine wertvolle Informationsquelle über invasive Arten darstellt und dass soziale Medien im Allgemeinen traditionelle Umweltinformationen ergänzen könnten. Twitter ist eine reichhaltige Quelle von primären Biodiversitätsbeobachtungen, einschließlich solcher zu eingebürgerten invasiven Arten. Zusätzlich kann gezeigt werden, dass die analysierten Twitterinhalte für die untersuchten Arten markante Themen- und Informationsprofile aufweisen, die wichtige Beiträge im Management invasiver Arten leisten können. Allgemein zeigt die Studie, dass einerseits das Potential von „Citizen Science“ im forstlichen Monitoring derzeit nicht ausgeschöpft wird, aber andererseits mit denjenigen Nutzern, die Biodiversitätsbeobachtungen auf Twitter teilen, eine große Zahl von Individuen mit einem Interesse an Umweltbeobachtungen zur Verfügung steht, die auf der Basis ihres dokumentierten Interesses unter Umständen für bewusst geplante „Citizen Science“-Projekte mobilisiert werden könnten. Zusammenfassend dokumentiert diese Studie, dass soziale Medien eine wertvolle Quelle für Umweltinformationen allgemein sind und eine verstärkte Untersuchung verdienen, letztlich mit dem Ziel, operative Systeme zur Unterstützung von Risikobewertungen in Echtzeit zu entwickeln.Major environmental, social and economic changes threatening the resilience of ecosystems world-wide and new demands on a broad range of forest ecosystem services present new challenges for forest management and monitoring. New risks and threats such as invasive alien species imply fundamental challenges for traditional forest management strategies, which have been based on assumptions of permanent ecosystem stability. Adaptive management and monitoring is called for to detect new threats and changes as early as possible, but this requires large-scale monitoring and monitoring resources remain a limiting factor. Accordingly, forest practitioners and scientists have begun to turn to public support in the form of “citizen science” to react flexibly to specific challenges and gather critical information. The emergence of ubiquitous mobile and internet technologies provides a new digital source of information in the form of so-called social media that essentially turns users of these media into environmental sensors and provides an immense volume of publicly accessible, ambient environmental information. Mining social media content, such as Facebook, Twitter, Wikis or Blogs, has been shown to make critical contributions to epidemic disease monitoring, emergency management or earthquake detection. Applications in the ecological domain remain anecdotal and a methodical exploration for this domain is lacking. Using the example of the micro-blogging service Twitter and invasive alien species in forest ecosystems, this study provides a methodical exploration and assessment of social media for forest monitoring. Social media mining is approached as an opportunistic citizen science model and the data, activities and contributors are analyzed in comparison to deliberate ecological citizen science monitoring. The results show that Twitter is a valuable source of information on invasive alien species and that social media in general could be a supplement to traditional monitoring data. Twitter proves to be a rich source of primary biodiversity observations including those of the selected invasive species. In addition, it is shown that Twitter content provides distinctive thematic profiles that relate closely to key characteristics of the explored invasive alien species and provide valuable insights for invasive species management. Furthermore, the study shows that while there are underutilized opportunities for citizen science in forest monitoring, the contributors of biodiversity observations on Twitter show a more than casual interest in this subject and represent a large pool of potential contributors to deliberate citizen science monitoring efforts. In summary, social online media are a valuable source for ecological monitoring information in general and deserve intensified exploration to arrive at operational systems supporting real-time risk assessments

Supporting quality in science communication: insights from the QUEST project

How to promote quality is a critical aspect to consider when re-examining science communication, analysed in the research carried out in QUEST project presented in this paper. Engaging key stakeholders in a co-design process - through interviews, focus groups, workshops and surveys - the research identified barriers to quality science communication and on the basis of these, proposes a series of tools and supporting documents that can serve as incentives toward quality science communication for different stakeholders across the fields of journalism, social media, and museum communication. Among these particularly important is training to also promote professionalism of communicators

Agate Fossil Beds National Monument, Paleontological Resources Management Plan (Public Version)

Executive Summary Since Agate Springs Ranch was founded by James H. Cook in 1887, exquisite examples of transitional Miocene mammalian fauna have been found along this stretch of the Niobrara River valley. Collectively these paleontological discoveries, along with the existing archeological and historical Native American collection, were the basis for establishing Agate Fossil Beds National Monument (AGFO) as a unit of the National Park System (NPS). The fossil remains from the Harrison and Anderson Ranch formations span a short, but important, time period within the Miocene Epoch. AGFO has provided science with an intimate look into North American mammalian evolution of the time that is matched nowhere else, with body fossils and trace fossils (burrows) of many mammals in excellent condition. Investigation of the paleontological resources at AGFO has been very limited since its establishment, but the opportunities for research and discovery are still substantial. Public and academic interest in the Monument’s paleontological resources are considerable. Although there are existing legal authorities, policies and guidelines regarding the management of paleontological resources, at both the departmental and agency levels, more specific guidance would be helpful for the management of AGFO’s non-renewable fossils. This document has been prepared to provide more specific guidance and recommendations for paleontological resources management at AGFO. The Introduction outlines the significance of AGFO’s paleontological resources and defines the purpose, need, and objectives for the Agate Fossil Beds National Monument Paleontological Resources Management Plan (PRMP). This plan also identifies the legal authorities, requirements, and mandates underpinning AGFO’s mission as a unit of the NPS, with special attention to authorities that address managing and preserving paleontological resources. Background Geology and Paleontology provides a basic park geologic description, discusses the scope of AGFO’s paleontological resources, and summarizes past paleontological work performed at the Monument. This information includes historical information from periods both before and after authorization of the Monument as a unit of the NPS. This section also presents the paleontological significance of AGFO and its specimens, such as how AGFO’s taxa are cornerstones of North American geochronology and biostratigraphy. Paleontological Resources Management begins by listing in greater detail the strategic objectives related to paleontological resources within the NPS and at AGFO. This section then proceeds to discuss the specific considerations related to paleontological resource inventories and monitoring along with management requirements (from policy and guidelines) specific to AGFO. This section discusses what a paleontology inventory is and why, when and how to conduct one; fundamentals of paleontological resource monitoring; the various types of threats to paleontological resources and how to mitigate them; and resource condition assessment and site monitoring protocols. It also addresses how to handle paleontological resources discovered or recovered during other park activities. Paleontological Research Management presents NPS and AGFO research goals, how to evaluate the scientific significance of research, and how to weigh the significance of research against other park mandates, operations, and goals. The section also includes a description of the permitting process, recommended park-specific permit conditions, and rules for overseeing collection and excavation. Museum Collections and Curation documents AGFO’s current paleontological collections, collections management and curation policies, AGFO’s photographic archives, collections from AGFO in external repositories, type specimens from AGFO, and Monument compliance with museum security policies. Interpretation discusses goals and current implementation for how AGFO interprets its paleontological resources for the public. This includes: the primary themes for interpretation; the exhibits, tools and programs used by AGFO to interpret fossil resources; the target audiences for the interpretive programs; teaching good stewardship of paleontological resources; and a discussion of public accessibility to AGFO, its interpretive materials, and the paleontological resources. Relation of Paleontological Resources to Other Park Programs provides an overview of how each park division may interact with paleontological resources and have their duties cross over with paleontological resource management related actions. It also discusses the potential impacts of paleontological resource management on other types of AGFO resources (archeological, biological, historical, and physical). Paleontological Resource Data Management discusses various paleontological resource records, datasets, and other archives. AGFO’s paleontological archives and library, as well as their current status, are discussed along with an overview of the type of contents stored within them. The NPS Paleontology Program Archives and Library, and how to exchange data and records between them and the AGFO archives, are also described. This section also reviews geospatial data available to AGFO and issues of sensitivity and confidentially related to paleontological resource data and information. Finally, the Summary, Conclusions, and Recommendations summarizes the work done on the AGFO PRMP, discusses ongoing and planned projects which assist in implementing the instructions and goals set out in this PRMP, and makes a variety of recommendations for future paleontological resource management at AGFO

Science Communication in South Africa

"Why do we need to communicate science? Is science, with its highly specialised language and its arcane methods, too distant to be understood by the public? Is it really possible for citizens to participate meaningfully in scientific research projects and debate? Should scientists be mandated to engage with the public to facilitate better understanding of science? How can they best communicate their special knowledge to be intelligible? These and a plethora of related questions are being raised by researchers and politicians alike as they have become convinced that science and society need to draw nearer to one another. Once the persuasion took hold that science should open up to the public and these questions were raised, it became clear that coming up with satisfactory answers would be a complex challenge. The inaccessibility of scientific language and methods, due to ever increasing specialisation, is at the base of its very success. Thus, translating specialised knowledge to become understandable, interesting and relevant to various publics creates particular perils. This is exacerbated by the ongoing disruption of the public discourse through the digitisation of communication platforms. For example, the availability of medical knowledge on the internet and the immense opportunities to inform oneself about health risks via social media are undermined by the manipulable nature of this technology that does not allow its users to distinguish between credible content and misinformation. In countries around the world, scientists, policy-makers and the public have high hopes for science communication: that it may elevate its populations educationally, that it may raise the level of sound decision-making for people in their daily lives, and that it may contribute to innovation and economic well-being. This collection of current reflections gives an insight into the issues that have to be addressed by research to reach these noble goals, for South Africa and by South Africans in particular.

Putting responsible research and innovation into practice at a local level in South Africa

Chapter 3 in the book Science Communication in South Africa.Published by African Minds