Using Social Media Data to Map Mortar Shells Falling in Damascus, Syria

Journalists increasingly use social media data to infer and report public opinion by quoting social media posts, identifying trending topics, and reporting general sentiment. In contrast to traditional approaches of inferring public opinion, citizens are often unaware of how their publicly available social media data is being used and how public opinion is constructed using social media analytics. In this exploratory study based on a census-weighted online survey of Canadian adults (N=1,500), we examine citizens’ perceptions of journalistic use of social media data. We demonstrate that: (1) people find it more appropriate for journalists to use aggregate social media data rather than personally identifiable data; (2) people who use more social media are more likely to positively perceive journalistic use of social media data to infer public opinion; and (3) the frequency of political posting is positively related to acceptance of this emerging journalistic practice, which suggests some citizens want to be heard publicly on social media while others do not. We provide recommendations for journalists on the ethical use of social media data and social media platforms on opt-in functionality.

BackgroundA scoping review to characterize the literature on the use of conversations in social media as a potential source of data for detecting adverse events (AEs) related to health products.MethodsOur specific research questions were (1) What social media listening platforms exist to detect adverse events related to health products, and what are their capabilities and characteristics? (2) What is the validity and reliability of data from social media for detecting these adverse events? MEDLINE, EMBASE, Cochrane Library, and relevant websites were searched from inception to May 2016. Any type of document (e.g., manuscripts, reports) that described the use of social media data for detecting health product AEs was included. Two reviewers independently screened citations and full-texts, and one reviewer and one verifier performed data abstraction. Descriptive synthesis was conducted.ResultsAfter screening 3631 citations and 321 full-texts, 70 unique documents with 7 companion reports available from 2001 to 2016 were included. Forty-six documents (66%) described an automated or semi-automated information extraction system to detect health product AEs from social media conversations (in the developmental phase). Seven pre-existing information extraction systems to mine social media data were identified in eight documents. Nineteen documents compared AEs reported in social media data with validated data and found consistent AE discovery in all except two documents. None of the documents reported the validity and reliability of the overall system, but some reported on the performance of individual steps in processing the data. The validity and reliability results were found for the following steps in the data processing pipeline: data de-identification (n = 1), concept identification (n = 3), concept normalization (n = 2), and relation extraction (n = 8). The methods varied widely, and some approaches yielded better results than others.ConclusionsOur results suggest that the use of social media conversations for pharmacovigilance is in its infancy. Although social media data has the potential to supplement data from regulatory agency databases; is able to capture less frequently reported AEs; and can identify AEs earlier than official alerts or regulatory changes, the utility and validity of the data source remains under-studied.Trial registrationOpen Science Framework (https://osf.io/kv9hu/).

BackgroundSchizophrenia is a disease associated with high burden, and improvement in care is necessary. Artificial intelligence (AI) has been used to diagnose several medical conditions as well as psychiatric disorders. However, this technology requires large amounts of data to be efficient. Social media data could be used to improve diagnostic capabilities.ObjectiveThe objective of our study is to analyze the current capabilities of AI to use social media data as a diagnostic tool for psychotic disorders.MethodsA systematic review of the literature was conducted using several databases (PubMed, Embase, Cochrane, PsycInfo, and IEEE Xplore) using relevant keywords to search for articles published as of November 12, 2021. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria to identify, select, and critically assess the quality of the relevant studies while minimizing bias. We critically analyzed the methodology of the studies to detect any bias and presented the results.ResultsAmong the 93 studies identified, 7 studies were included for analyses. The included studies presented encouraging results. Social media data could be used in several ways to care for patients with schizophrenia, including the monitoring of patients after the first episode of psychosis. We identified several limitations in the included studies, mainly lack of access to clinical diagnostic data, small sample size, and heterogeneity in study quality. We recommend using state-of-the-art natural language processing neural networks, called language models, to model social media activity. Combined with the synthetic minority oversampling technique, language models can tackle the imbalanced data set limitation, which is a necessary constraint to train unbiased classifiers. Furthermore, language models can be easily adapted to the classification task with a procedure called “fine-tuning.”ConclusionsThe use of social media data for the diagnosis of psychotic disorders is promising. However, most of the included studies had significant biases; we therefore could not draw conclusions about accuracy in clinical situations. Future studies need to use more accurate methodologies to obtain unbiased results.

Data acquired from site surveys provide an essential resource to various exploration and production activities, such as field development planning, well positioning, and subsea equipment positioning. For many years, site survey data has been delivered as a Computer-aided Design (CAD) product. As operators move into a Geographic Information Systems (GIS) environment, shortfalls from CAD-based workflows have become obvious. This paper presents a method of managing site survey data based on geo-information management principles and practices. The methodology presented in this paper defines a GIS data model for seabed survey data interpretation that satisfies GIS query, presentation, and data exchange purposes. This paper also defines a standard for GIS seabed survey data deliverables from contractors to operators, including methods and processes to move the model to/from CAD-based applications. The implementation of the data model has proven to be a relative success and allows for a better integration of subsurface applications internally. The results have helped define an efficient contractor-operator data exchange process by eliminating data ambiguity and enabling seamless GIS data transfer. Results have also produced a more intelligent seabed survey GIS dataset with rules and behaviors capability offered by an ArcGIS Geodatabase embedded into the data model. Use of a standard GIS data model and a standard list of GIS data deliverables required from contractors to operators will allow project teams to fully embrace the power of GIS data integration and allow nearly seamless and lossless movement of CAD/GIS representations of the model. Introduction In order to reach maximum potential benefit of data provided from seabed site surveys throughout the E & P lifecylcle (Figure 1), there is a need to ensure that data delivered from survey contractors to operators is done so in a structured, efficient, and consistent manner.

Pharmacovigilance is essential for protecting patient health by monitoring and managing medication-related risks. Traditional methods like spontaneous reporting systems and clinical trials are valuable for identifying adverse drug events, but face delays in data access. Social media platforms, with their real-time data, offer a novel avenue for pharmacovigilance by providing a wealth of user-generated content on medication usage, adverse drug events, and public sentiment. However, the unstructured nature of social media content presents challenges in data analysis, including variability and potential biases. Advanced techniques like natural language processing and machine learning are increasingly being employed to extract meaningful information from social media data, aiding in early adverse drug event detection and real-time medication safety monitoring. Ensuring data reliability and addressing ethical considerations are crucial in this context. This review examines the existing literature on the use of social media data for drug safety analysis, highlighting the platforms involved, methodologies applied, and research questions explored. It also discusses the challenges, limitations, and future directions of this emerging field, emphasizing the need for ethical principles, transparency, and interdisciplinary collaboration to maximize the potential of social media in enhancing pharmacovigilance efforts.

Social Media (SM) data are growing, and SM is becoming an acceptable part of daily life for billions of people around the world. Extracting information from Social Networking Sites (SNS) can provide great challenges as well as opportunities. Using SM data beyond day-to-day communication can provide additional values. There is much research and many products that are dedicated to take SNS beyond communication channels. In our research, we are going beyond specific tools inherent to the SM tools, such as Hashtag mentions and Like counts. Instead it will use text-based modeling, data mining techniques, natural process language, machine language, etc. to understand SM content to produce numeric ratings. The final contribution of this research is building a SM users' rating model for an event using SM data. At this point of our research, we are laying out a road map.

A challenge for studies assessing routine activities theory is accounting for the spatial and temporal confluence of offenders and targets given that people move about during the daytime and nighttime. We propose exploiting social media (Twitter) data to construct estimates of the population at various locations at different times of day, and assess whether these estimates help predict the amount of crime during two-hour time periods over the course of the day. We address these questions using crime data for 97,428 blocks in the Southern California region, along with geocoded information on tweets in the region over an eight month period. The results show that this measure of the temporal ambient population helps explain the level of crime in blocks during particular time periods. The use of social media data appear promising for testing various implications of routine activities and crime pattern theories, given their explicit spatial and temporal nature.

The use of social media data to gain insights into public phenomena is a potentially powerful tool. We present the use of social media data analysis, connected with crime and public safety incidents, to better understand reoccurring topics and potentially feed into an automated incident detection application. We collected a size-able dataset of Twitter posts (more than 60,000) over a 3 month period by monitoring crime and public safety related keywords linked to accounts. By splitting the data into two categories we are able to extract topics as well as compare and contrast how monitoring official crime and public safety accounts differs from monitoring individuals and organisations that may not be part of that group. Finally we discuss a prototype application, which uses social media data as well as locations to calculate metrics using potential crime and public safety related incidents.

This paper presents a preliminary empirical evaluation of the strategic importance of infusing Twitter social media data into classifications of small areas, as a way of moving beyond the nighttime residential geographies of conventional geodemographic classifications. We attempt an empirically based critique of the merits and drawbacks of the use of social media data, in which the value of high spatial and temporal granularity of revealed activity patterns is contrasted with the paucity of individual attribute information. We apply new and novel methods to enrich the profiles of Twitter users in order to generalize about activity patterns in London, our case-study city. More insidious problems in the use of social media data arise from the as-yet-unknown sources and operation of bias in their user bases. Our contribution is to begin to identify and assess the biases inherent in social media usage in social research, and use these to evaluate their deployment in research applications.

We explored the transformation of social media content into three radically different formats; a book, a triptych of photographs and film.Using interviews, we investigated users' responses to their remediated data.Our findings were used to develop a digital curation process to assist the development of different tools for social media analysis and display.Our paper establishes design implications to aid reflection on social media use through remediation. Increasingly our digital traces are providing new opportunities for self-reflection. In particular, social media (SM) data can be used to support self-reflection, but to what extent is this affected by the form in which SM data is presented? Here, we present three studies where we work with individuals to transform or remediate their SM data into a physical book, a photographic triptych and a film. We describe the editorial decisions that take place as part of the remediation process and show how the transformations allow users to reflect on their digital identity in new ways. We discuss our findings in terms of the application of Goffman's (1959) self-presentation theories to the SM context, showing that a fluid rather than bounded interpretation of our social media spaces may be appropriate. We argue that remediation can contribute to the understanding of digital self and consider the design implications for new SM systems designed to support self-reflection.

Compared to natural language processing research investigating suicide risk prediction with social media (SM) data, research utilizing data from clinical settings are scarce. However, the utility of models trained on SM data in text from clinical settings remains unclear. In addition, commonly used performance metrics do not directly translate to operational value in a real-world deployment. The objectives of this study were to evaluate the utility of SM-derived training data for suicide risk prediction in a clinical setting and to develop a metric of the clinical utility of automated triage of patient messages for suicide risk. Using clinical data, we developed a Bidirectional Encoder Representations from Transformers-based suicide risk detection model to identify messages indicating potential suicide risk. We used both annotated and unlabeled suicide-related SM posts for multi-stage transfer learning, leveraging customized contemporary learning rate schedules. We also developed a novel metric estimating predictive models' potential to reduce follow-up delays with patients in distress and used it to assess model utility. Multi-stage transfer learning from SM data outperformed baseline approaches by traditional classification performance metrics, improving performance from 0.734 to a best F1 score of 0.797. Using this approach for automated triage could reduce response times by 15 minutes per urgent message. Despite differences in data characteristics and distribution, publicly available SM data benefit clinical suicide risk prediction when used in conjunction with contemporary transfer learning techniques. Estimates of time saved due to automated triage indicate the potential for the practical impact of such models when deployed as part of established suicide prevention interventions. This work demonstrates a pathway for leveraging publicly available SM data toward improving risk assessment, paving the way for better clinical care and improved clinical outcomes.

City/regional authorities are responsible for designing and structuring the urban morphology based on the desired land use activities. One of the key concerns regarding urban planning is to establish certain development goals, such as the real gross domestic product (GDP). In Canada, the gross national income (GNI) mainly relies on the mining and manufacturing industries. In order to estimate the impact of city development, this study aims to utilize remote sensing and Geographic Information System (GIS) techniques to assess the relationship between the built-up area and the reported real GDP of seven major cities in Canada. The objectives of the study are: (1) to investigate the use of regression analysis between the built-up area derived from Landsat images and the industrial area extracted from Geographic Information System (GIS) data; and (2) to study the relationship between the built-up area and the socio-economic data (i.e., real GDP, total population and total employment). The experimental data include 42 multi-temporal Landsat TM images and 42 land use GIS vector datasets obtained from year 2005 to 2010 during the summer season (June, July and August) for seven major cities in Canada. The socio-economic data, including the real GDP, the total population and the total employment, are obtained from the Metropolitan Housing Outlook during the same period. Both the Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) were used to determine the built-up areas. Those high built-up values within the industrial areas were acquired for further analysis. Finally, regression analysis was conducted between the real GDP, the total population, and the total employment with respect to the built-up area. Preliminary findings showed a strong linear relationship (R2= 0.82) between the percentage of built-up area and industrial area within the corresponding city. In addition, a strong linear relationship (R2= 0.8) was found between the built-up area and socio-economic data. Therefore, the study justifies the use of remote sensing and GIS data to model the socio-economic data (i.e., real GDP, total population and total employment). The research findings can contribute to the federal/municipal authorities and act as a generic indicator for targeting a specific real GDP with respect to industrial areas.

Geographic Information Systems (GIS) data are crucial for many applications such as disaster response, damage assessment, and recovery efforts. The 2010 Haiti earthquake destroyed buildings where GIS data were stored and many of the relief organizations ultimately depended on data retrieved from outside the country to do their work. The lesson learnt from this experience is that open access data is necessary in assisting tasks in such situations and therefore the evaluation of such data for its quality is important. This research evaluates OpenStreetMap (OSM) data that can be and is used by various organizations. The methodology followed is based on the International Organization for Standardization (ISO) Geographic Information-Quality Principles (ISO19113). According to the ISO19113 standard, the data quality elements are: completeness, logical consistency, positional accuracy, thematic accuracy, and temporal accuracy. The first four elements noted above were tested in this study. OSM data was compared with official data from Abu Dhabi city. Results show that the positional accuracy of OSM data meets the American Society for Photogrammetry and Remote Sensing (ASPRS) standards for 1:20,000-scale maps. This indicates that OSM data could be used for many applications, taking into account the hurdles in GIS data acquisition, cost, availability, licensing, distribution policies, and documentation. The article concludes with a call for incorporation of geospatial data services and data quality initiatives in libraries.

Massive amounts of data are being generated on social media sites, such as Twitter and Facebook. People from all walks of life share data about social events, express opinions, discuss their interests, publicize businesses, recommend products, and, explicitly or implicitly, reveal personal information. This workshop will focus on the use of social media data for creating models of individual users from the content that they publish. Deeper understanding of user behavior and associated attributes can benefit a wide range of intelligent applications, such as social recommender systems and expert finders, as well as provide the foundation in support of novel user interfaces (e.g., actively engaging the crowd in mixed-initiative question-answering systems). These applications and interfaces may offer significant benefits to users across a wide variety of domains, such as retail, government, healthcare and education. User modeling from public social media data may also reveal information that users would prefer to keep private. Such concerns are particularly important because individuals do not have complete control over the information they share about themselves. For example, friends of a user may inadvertently divulge private information about that user in their own posts. In this workshop we will also discuss possible mechanisms that users might employ to monitor what information has been revealed about themselves on social media and obfuscate any sensitive information that has been accidentally revealed.

This dataset provides a high-resolution spatial classification of urban morphology on Reunion Island, France, using a 100 m x 100 m grid based on the Local Climate Zone (LCZ) framework. The data were generated through a combination of satellite imagery analysis using the World Urban Database and Access Portal Tools (WUDAPT) method and Geographic Information System (GIS) data integration. The classification was refined using building height and surface fraction data from the BD TOPO 2018 database, improving the accuracy of urban form representation.The dataset is provided in shapefile format, facilitating its use in GIS software by urban planners, researchers, and policymakers. Each grid point contains geospatial and morphological attributes, including LCZ classification, average building height (m), and built surface fraction (%). These parameters allow researchers to spatially correlate urban morphology with observed temperature data, supporting targeted urban heat island (UHI) studies. The most compact LCZ types (LCZ 1, 2, and 3) are mapped in detail, highlighting zones on the island most exposed to climatic risks. These high-risk areas should be identified and prioritized for climate mitigation strategies.Beyond UHI analysis, the dataset allows for the identification of land use patterns and urban growth trends by analyzing the spatial distribution of LCZ across the island. Areas of recent expansion, often characterized by transitions from natural zones (LCZ 11, 14) to LCZ 6 or 9 (low-rise and sparsely built, can be geolocated and compared with zoning regulations. This offers urban planners a tool to evaluate whether current development aligns with local urban containment policies and to identify zones at risk of unregulated sprawl.Because the LCZ classification system is internationally standardized, the dataset also supports comparative urban climate studies. It can be directly used to contrast Reunion’s urban structure with other small island developing states, such as Mauritius, in order to evaluate similarities and differences in urbanization dynamics under similar climatic constraints.