Crowdbreaks: Tracking Health Trends using Public Social Media Data and Crowdsourcing

In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams. At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community. Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labelling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labelling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work introduces the technical aspects of the platform and explores its future use cases

Analisis Media Sosial Facebook Lite dengan tools Forensik menggunakan Metode NIST

Social Media is becoming very popular among the public today, and the increasing number of social media use has of course a good or bad impact on the course of human life, for example the bad impact is doing cyberbully or chating on social media. Digital forensics is one of the sciences for how to catch criminals in digital which will be needed in evidence in court. Social media criminals need Smartphones to commit digital cybercrime. This research will raise evidence of digital crimes on the Facebook Lite application using forensics. In this study, the forensic tool that will be used is the MOBILEedit Forensic Pro forensic tools with the help of using methods NIST National Institute Of Standars Techlogogy. NIST has a good workflow for extracting digital forensic data. The research results will be obtained in the form of accounts Id, audio, conversations, and image

Social Media as a Healthcare Tool: Case Study Analysis of Factors Influencing Pediatric Clinicians\u27 Behavioral Intent to Adopt Social Media for Patient Communication and Engagement

Social media aids communication among users worldwide. However, a notable gap exist among social media users, healthcare professionals utilizing social media in the work place. While the concept of harnessing social media as a professional tool is not novel, healthcare professionals have yet to embrace the practice as standard workflow. This study identifies factors influencing clinicians\u27 behavioral intent to adopt social media for patient engagement and communication. A new framework, the Healthcare Social Media Adoption Framework (HSMA), guided this mixed-method approach to assess 7 factors identified by theory and literature as adoption influencers. A custom, web-based survey collected data from 60 full-time, pediatric clinicians (47 quantitative) at the case institution (a pediatric hospital). Additionally, individual interviews of 6 participants provided their prospective on using social media for patient communications and engagement. Results: Privacy concerns were the only statically significant factor; with an inverse relationship to positive adoption intent, indicating higher privacy concerns influence lower behavioral intent to adopt social media for patient engagement and communication. The qualitative analysis revealed privacy concerns encompass two themes, personal privacy for patient and providers (boundaries), and cybersecurity. The qualitative inputs also uncovered perceived unprofessionalism as a new factor influencing clinician adoption. The implications for these findings indicate a need for both healthcare organizations and healthcare regulators to establish cyber-security defenses for security and use protocols for privacy to aid the diffusion and adoption acceptance of social media use by pediatric healthcare professionals. This research has contributed in four areas: 1) fill a knowledge gap by identifying new factors that influence the behavioral intent of pediatric clinicians to adopt social media; 2) confirm/reject behavioral intent influences found in the literature; 3) formulated a new HSMA framework that measures functional, cognitive, and social aspects of social media adoption; and 4) prioritizes policies and global standard focus

Screening the stones of Venice: Mapping social perceptions of cultural significance through graph-based semi-supervised classification

Mapping cultural significance of heritage properties in urban environment from the perspective of the public has become an increasingly relevant process, as highlighted by the 2011 UNESCO Recommendation on the Historic Urban Landscape (HUL). With the ubiquitous use of social media and the prosperous developments in machine and deep learning, it has become feasible to collect and process massive amounts of information produced by online communities about their perceptions of heritage as social constructs. Moreover, such information is usually inter-connected and embedded within specific socioeconomic and spatiotemporal contexts. This paper presents a methodological workflow for using semi-supervised learning with graph neural networks (GNN) to classify, summarize, and map cultural significance categories based on user-generated content on social media. Several GNN models were trained as an ensemble to incorporate the multi-modal (visual and textual) features and the contextual (temporal, spatial, and social) connections of social media data in an attributed multi-graph structure. The classification results with different models were aligned and evaluated with the prediction confidence and agreement. Furthermore, message diffusion methods on graphs were proposed to aggregate the post labels onto their adjacent spatial nodes, which helps to map the cultural significance categories in their geographical contexts. The workflow is tested on data gathered from Venice as a case study, demonstrating the generation of social perception maps for this UNESCO World Heritage property. This research framework could also be applied in other cities worldwide, contributing to more socially inclusive heritage management processes. Furthermore, the proposed methodology holds the potential of diffusing any human-generated location-based information onto spatial networks and temporal timelines, which could be beneficial for measuring the safety, vitality, and/or popularity of urban spaces

A framework for interrogating social media images to reveal an emergent archive of war

The visual image has long been central to how war is seen, contested and legitimised, remembered and forgotten. Archives are pivotal to these ends as is their ownership and access, from state and other official repositories through to the countless photographs scattered and hidden from a collective understanding of what war looks like in individual collections and dusty attics. With the advent and rapid development of social media, however, the amateur and the professional, the illicit and the sanctioned, the personal and the official, and the past and the present, all seem to inhabit the same connected and chaotic space.However, to even begin to render intelligible the complexity, scale and volume of what war looks like in social media archives is a considerable task, given the limitations of any traditional human-based method of collection and analysis. We thus propose the production of a series of ‘snapshots’, using computer-aided extraction and identification techniques to try to offer an experimental way in to conceiving a new imaginary of war. We were particularly interested in testing to see if twentieth century wars, obviously initially captured via pre-digital means, had become more ‘settled’ over time in terms of their remediated presence today through their visual representations and connections on social media, compared with wars fought in digital media ecologies (i.e. those fought and initially represented amidst the volume and pervasiveness of social media images).To this end, we developed a framework for automatically extracting and analysing war images that appear in social media, using both the features of the images themselves, and the text and metadata associated with each image. The framework utilises a workflow comprising four core stages: (1) information retrieval, (2) data pre-processing, (3) feature extraction, and (4) machine learning. Our corpus was drawn from the social media platforms Facebook and Flickr

Towards the design of a platform for abuse detection in OSNs using multimedial data analysis

Online social networks (OSNs) are becoming increasingly popular every day. The vast amount of data created by users and their actions yields interesting opportunities, both socially and economically. Unfortunately, these online communities are prone to abuse and inappropriate behaviour such as cyber bullying. For victims, this kind of behaviour can lead to depression and other severe problems. However, due to the huge amount of users and data it is impossible to manually check all content posted on the social network. We propose a pluggable architecture with reusable components, able to quickly detect harmful content. The platform uses text-, image-, audio- and video-based analysis modules to detect inappropriate content or high risk behaviour. Domain services aggregate this data and flag user profiles if necessary. Social network moderators need only check the validity of the flagged profiles. This paper reports upon key requirements of the platform, the architectural components and important challenges

Creating, Doing, and Sustaining OER: Lessons from Six Open Educational Resource Projects

The development of free-to-use open educational resources (OER) has generated a dynamic field of widespread interest and study regarding methods for creating and sustaining OER. To help foster a thriving OER movement with potential for knowledge-sharing across program, organizational and national boundaries, the Institute for Knowledge Management in Education (ISKME), developed and conducted case study research programs in collaboration with six OER projects from around the world. Embodying a range of challenges and opportunities among a diverse set of OER projects, the case studies intended to track, analyze and share key developments in the creation, use and reuse of OER. The specific cases include: CurriculumNet, Curriki, Free High School Science Texts (FHSST), Training Commons, Stanford Encyclopedia of Philosophy (SEP), and Teachers' Domain