Other affiliations: University of Jyväskylä
Bio: Tuomo Hiippala is an academic researcher from University of Helsinki. The author has contributed to research in topics: Social media & Multimodality. The author has an hindex of 12, co-authored 48 publications receiving 697 citations. Previous affiliations of Tuomo Hiippala include University of Jyväskylä.
TL;DR: Combined with other data sources and carefully considering the biases and ethical issues, social media data can provide a complementary and cost-efficient information source for addressing the grand challenges of biodiversity conservation in the Anthropocene epoch.
Abstract: Improved understanding of human-nature interactions is crucial to conservation science and practice, but collecting relevant data remains challenging. Recently, social media have become an increasingly important source of information on human-nature interactions. However, the use of advanced methods for analysing social media is still limited, and social media data are not used to their full potential. In this article, we present available sources of social media data and approaches to mining and analysing these data for conservation science. Specifically, we (i) describe what kind of relevant information can be retrieved from social media platforms, (ii) provide a detailed overview of advanced methods for spatio-temporal, content and network analyses, (iii) exemplify the potential of these approaches for real-world conservation challenges, and (iv) discuss the limitations of social media data analysis in conservation science. Combined with other data sources and carefully considering the biases and ethical issues, social media data can provide a complementary and cost-efficient information source for addressing the grand challenges of biodiversity conservation in the Anthropocene epoch.
TL;DR: To unlock this potential, social media platforms must be engaged to share their data and actively collaborate in the development of real-time monitoring tools that can be used to automatically identify content pertaining to the illegal wildlife trade, and to report this content to enforcers.
Abstract: To the Editor — Illegal trade in wildlife is booming on e-commerce platforms1, the ‘dark web’2 and social media1,3. The ease of access and high number of users of social media make it a particularly concerning venue1,3,4. Wildlife dealers use social media to release photos and information about products to attract customers and to market their products to networks of contacts. We currently lack the tools for automated monitoring of high-volume data that are needed to investigate and prevent this illegal trade, but machine-learning algorithms offer a way forward. Operating within the broader field of artificial intelligence, the concept of machine learning refers to algorithms that learn from data without human guidance. Deep-learning algorithms5 are a family of these algorithms that are highly successful in classifying image contents and locating individual objects within them, and in processing natural language. Applying these techniques to social media data allows human behaviour to be investigated on an unprecedented scale. Yet these techniques and data sources are still rarely used to address drivers of the biodiversity crisis6. Many social media platforms provide an application programming interface that allows access to user-generated text, images and videos, as well as to accompanying metadata, such as where and when the content was uploaded, and connections between users. Processing such data manually is inefficient and time consuming, but machine-learning algorithms can be trained to filter this content to identify relevant information (see Fig. 1 for an example). These algorithms can be trained to detect which species or wildlife products, such as horns or scales, appear in an image or video, while also classifying their setting, such as a natural habitat or a marketplace. When processing video, algorithms can use audio clues, such as identifying bird species by their songs and calls, as well as interrogating the image stream. Natural language processing can be used to infer the meaning of a verbal description, for example, whether an animal or plant is for sale or observed in nature, and to classify the sentiment and preferences of social media users. To unlock this potential, social media platforms must be engaged to share their data and actively collaborate in the development of real-time monitoring tools that can be used to automatically identify content pertaining to the illegal wildlife trade, and to report this content to enforcers. Furthermore, machine-learning algorithms need humanverified training data. Such training datasets may be generated through crowd-sourcing initiatives, and collaborations between scientists and enforcers may further improve the algorithms’ performance. Together with advances in artificial intelligence that will refine the algorithms themselves, such efforts
•10 Apr 2017
TL;DR: In this article, the first foundational introduction to the practice of analysing multimodality, covering the full breadth of media and situations in which multimodal needs to be a concern.
Abstract: This textbook provides the first foundational introduction to the practice of analysing multimodality, covering the full breadth of media and situations in which multimodality needs to be a concern. Readers learn via use cases how to approach any multimodal situation and to derive their own specifically tailored sets of methods for conducting and evaluating analyses. Extensive references and critical discussion of existing approaches from many disciplines and in each of the multimodal domains addressed are provided. The authors adopt a problem-oriented perspective throughout, showing how an appropriate foundation for understanding multimodality as a phenomenon can be used to derive strong methodological guidance for analysis as well as supporting the adoption and combination of appropriate theoretical tools. Theoretical positions found in the literature are consequently always related back to the purposes of analysis rather than being promoted as valuable in their own right. By these means the book establishes the necessary theoretical foundations to engage productively with today's increasingly complex combinations of multimodal artefacts and performances of all kinds.
TL;DR: The results show that user-generated geographic information sources provide useful insights about being in, moving through and perceiving urban green spaces, as long as evident limitations and sample biases are acknowledged.
Abstract: Parks and other green spaces are an important part of sustainable, healthy and socially equal urban environment. Urban planning and green space management benefit from information about green space use and values, but such data are often scarce and laborious to collect. Temporally dynamic geographic information generated by different mobile devices and social media platforms are a promising source of data for studying green spaces. User-generated data have, however, platform specific characteristics that limit their potential use. In this article, we compare the ability of different user-generated data sets to provide information on where, when and how people use and value urban green spaces. We compare four types of data: social media, sports tracking, mobile phone operator and public participation geographic information systems (PPGIS) data in a case study from Helsinki, Finland. Our results show that user-generated geographic information sources provide useful insights about being in, moving through and perceiving urban green spaces, as long as evident limitations and sample biases are acknowledged. Social media data highlight patterns of leisure time activities and allow further content analysis. Sports tracking data and mobile phone data capture green space use at different times of the day, including commuting through the parks. PPGIS studies allow asking specific questions from active participants, but might be limited in spatial and temporal extent. Combining information from multiple user-generated data sets complements traditional data sources and provides a more comprehensive understanding of green space use and preferences.
TL;DR: Machine learning can be used to automatically monitor and assess illegal wildlife trade on social media platforms, according to a report from the Pew Research Center.
Abstract: Article impact statement: Machine learning can be used to automatically monitor and assess illegal wildlife trade on social media platforms.
01 Jan 2002
01 Jan 2015
TL;DR: Fawcett, M.K.Halliday, Sydney M. Lamb and Adam Makkai as discussed by the authors presented a systemic-functional interpretation of the nature and ontogenesis of dialogue.
Abstract: List of Figures List of Tables Foreword Introduction Robin P. Fawcett, M.A.K. Halliday, Sydney M. Lamb and Adam Makkai 1 Language as Code and Language as Behaviour: A Systemic-Functional Interpretation of the Nature and Ontogenesis of Dialogue M.A.K. Halliday 2 Metaphors of Information John Regan 3 How Universal is a Localist Hypothesis? A Linguistic Contribution to the Study of 'Semantic Styles' of Language Yoshihiko Ikegami 4 Some Speculations on Language Contact in a Wider Setting Jeffrey Ellis 5 Ways of Saying: Ways of Meaning Ruqaiya Hasan Index
TL;DR: Dollimore as discussed by the authors argues that critical theorists should strive to understand the contradictions within our lives and our literature and explore the daemonic power of the subjects that offend our sense of tradition.
Abstract: but the threat they bring to artistic culture. From his opening mockery of the literary establishment’s tendency to theorize the world in terms of desire or gender to his disapproval of those who venerate art while denying its validity in the same breath, Jonathan Dollimore has created an easily understood, albeit at times too theoretical, synthesis of the literary and the experiential in Sex, Literature and Censorship. His arguments on critical theory do not necessarily reject the concept of theory; rather, he argues that critical theorists should strive to understand the contradictions within our lives and our literature and explore the daemonic power of the subjects that offend our sense of tradition.
Carleton University1, Michigan State University2, University of Saskatchewan3, University of California, Santa Barbara4, Federation University Australia5, University of Colorado Boulder6, McMaster University7, Mount Allison University8, University of Washington9, Cardiff University10, Queen's University11, Leibniz Association12, University of Hong Kong13
TL;DR: Efforts to reverse global trends in freshwater degradation now depend on bridging an immense gap between the aspirations of conservation biologists and the accelerating rate of species endangerment.
Abstract: In the 12 years since Dudgeon et al. (2006) reviewed major pressures on freshwater ecosystems, the biodiversity crisis in the world’s lakes, reservoirs, rivers, streams and wetlands has deepened. While lakes, reservoirs and rivers cover only 2.3% of the Earth’s surface, these ecosystems host at least 9.5% of the Earth’s described animal species. Furthermore, using the World Wide Fund for Nature’s Living Planet Index, freshwater population declines (83% between 1970 and 2014) continue to outpace contemporaneous declines in marine or terrestrial systems. The Anthropocene has brought multiple new and varied threats that disproportionately impact freshwater systems. We document 12 emerging threats to freshwater biodiversity that are either entirely new since 2006 or have since intensified: (i) changing climates; (ii) e-commerce and invasions; (iii) infectious diseases; (iv) harmful algal blooms; (v) expanding hydropower; (vi) emerging contaminants; (vii) engineered nanomaterials; (viii) microplastic pollution; (ix) light and noise; (x) freshwater salinisation; (xi) declining calcium; and (xii) cumulative stressors. Effects are evidenced for amphibians, fishes, invertebrates, microbes, plants, turtles and waterbirds, with potential for ecosystem-level changes through bottom-up and top-down processes. In our highly uncertain future, the net effects of these threats raise serious concerns for freshwater ecosystems. However, we also highlight opportunities for conservation gains as a result of novel management tools (e.g. environmental flows, environmental DNA) and specific conservation-oriented actions (e.g. dam removal, habitat protection policies,managed relocation of species) that have been met with varying levels of success.Moving forward, we advocate hybrid approaches that manage fresh waters as crucial ecosystems for human life support as well as essential hotspots of biodiversity and ecological function. Efforts to reverse global trends in freshwater degradation now depend on bridging an immense gap between the aspirations of conservation biologists and the accelerating rate of species endangerment.
11 Dec 2018