2011 — 2015 |
Flach, John (co-PI) [⬀] Sheth, Amit [⬀] Shalin, Valerie |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Socs: Collaborative Research: Social Media Enhanced Organizational Sensemaking in Emergency Response @ Wright State University
This collaborative research leverages expertise of researchers at Wright State University (IIS-1111182) and Ohio State University (IIS-1111118). Online social networks and always-connected mobile devices have created an immense opportunity that empowers citizens and organizations to communicate and coordinate effectively in the wake of critical events. Specifically, there have been many isolated examples of using Twitter to provide timely and situational information about emergencies to relief organizations, and to conduct ad-hoc coordination. However, there are few attempts that try to understand the full ramifications of using social networks in a more concerted manner for effective organizational sensemaking. This project aims to conduct multidisciplinary research involving computer and social scientists fill this gap.
This project seeks to leverage Twitter posts (tweets) as the primary source of citizen inputs and couple relevant content and network information along with microworld simulations involving human role players to measure effectiveness of various organized sensemaking strategies. To arrive at meaningful summaries of citizen input, tweet content is analyzed using a semantic content analysis by combining natural language techniques that are suitably fused with existing knowledge bases (GeoNames, Wikipedia). Content analysis is further enhanced by innovatively combining it with the dynamic analysis of the twitter network to realize concise and trustworthy information nuggets of potential interest to organizations and citizens. The resulting summaries will be fed to a suitably designed microworld simulation involving human actors to derive realistic settings for modeling disaster situations and typical organizational structures.
This project is expected to have a significant impact in the specific context of disaster and emergency response. However, elements of this research are expected to have much wider utility, for example in the domains of e-commerce, and social reform. From a computational perspective, this project introduces the novel paradigm of people-content-network analysis whose application is not just limited to organized sensemaking. For social scientists, it provides a platform that can be used to assess relative efficacy of various organizational structures using microworld simulations and is expected to provide new insights into the types of social network structures (mix of symmetric and asymmetric) that might be better suitable to propagate information in emergent situations. From an educational standpoint, the majority of funds will be used to train the next generation of interdisciplinary researchers drawn from the computational and social sciences. Research activities will also be integrated with graduate course work. Participation of underrepresented groups will be encouraged. Datasets and software developed as part of this project will be made available to the broader research community via the project page (http://knoesis.org/research/semspc/projects/socs).
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1 |
2015 — 2019 |
Liu, Desheng (co-PI) [⬀] Kubatko, Ethan (co-PI) [⬀] Shalin, Valerie Sheth, Amit (co-PI) [⬀] Parthasarathy, Srinivasan [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Hazards Sees: Social and Physical Sensing Enabled Decision Support For Disaster Management and Response
Infrastructure systems are a cornerstone of civilization. Damage to infrastructure from natural disasters such as an earthquake (e.g., Haiti, Japan), a hurricane (e.g., Katrina, Sandy), or a flood (e.g., Kashmir floods) can lead to significant economic loss and societal suffering. Human coordination and information exchange are at the center of damage control. This project aims to radically reform decision support systems for managing rapidly changing disaster situations by the integration of social, physical and hazard models. The researcher team will serve as a model for highly integrative and collaborative work among researchers in computer science, engineering, natural sciences, and the social sciences for research, education, and training of undergraduate and graduate students, including those from under-represented groups.
The team seeks to design novel, multi-dimensional, cross-modal aggregation and inference methods to compensate for the uneven coverage of sensing modalities across an affected region. They use data from social and physical sensors as input into an integrated model, from which they are designing a new methodology to predict and prioritize the consequences of damage; they are including both temporally and conceptually extended consequences of damage to people, civil infrastructure (transportation, power, waterways) and their components (e.g., bridges, traffic signals). They are developing innovative technology to support the identification of new background knowledge and structured data to improve object extraction, location identification correlation, and integration of relevant data across multiple sources and modalities (social, physical and Web). They use novel coupling of socio-linguistic and network analysis to identify important persons and objects, statistical and factual knowledge about traffic and transportation networks, and the resulting impact on hazard models (e.g. storm surge) and flood mapping. They are developing domain-grounded mechanisms to address pervasive trustworthiness and reliability concerns. Exemplar outcomes include specific tools for first-responders and recovery teams to aid in the prioritization of relief and repair efforts as well as improved flood response, urban mapping, and dynamic storm surge models. They also are providing interdisciplinary training of students, leveraging research in pedagogy in conjunction with Ohio State University's new undergraduate major in data analytics and Wright State University's Big and Smart Data graduate certificate program.
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0.979 |
2015 — 2018 |
Shalin, Valerie Sheth, Amit [⬀] Thirunarayan, Krishnaprasad |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Twc Sbe: Medium: Context-Aware Harassment Detection On Social Media @ Wright State University
As social media permeates our daily life, there has been a sharp rise in the use of social media to humiliate, bully, and threaten others, which has come with harmful consequences such as emotional distress, depression, and suicide. The October 2014 Pew Research survey shows that 73% of adult Internet users have observed online harassment and 40% have experienced it. The prevalence and serious consequences of online harassment present both social and technological challenges. This project identifies harassing messages in social media, through a combination of text analysis and the use of other clues in the social media (e.g., indications of power relationships between sender and receiver of a potentially harassing message.) The project will develop prototypes to detect harassing messages in Twitter; the proposed techniques can be adapted to other platforms, such as Facebook, online forums, and blogs. An interdisciplinary team of computer scientists, social scientists, urban and public affairs professionals, educators, and the participation of college and high schools students in the research will ensure wide impact of scientific research on the support for safe social interactions.
This project combines social science theory and human judgment of potential harassment examples from social media, in both school and workplace contexts, to operationalize the detection of harassing messages and offenders. It develops comprehensive and reliable context-aware techniques (using machine learning, text mining, natural language processing, and social network analysis) to glean information about the people involved and their interconnected network of relationships, and to determine and evaluate potential harassment and harassers. The key innovations of this work include: (1) identification of the generic language of insult, characterized by profanities and other general patterns of verbal abuse, and recognition of target-dependent offensive language involving sensitive topics that are personal to a specific individual or social circle; (2) prediction of harassment-specific emotion evoked in a recipient after reading messages by leveraging conversation history as well as sender's emotions; (3) recognition of a sender's malicious intent behind messages based on the aspects of power, truth (approximated by trust), and familiarity; (4) a harmfulness assessment of harassing messages by fusing aforementioned language, emotion, and intent factors; and (5) detection of harassers from their aggregated behaviors, such as harassment frequency, duration, and coverage measures, for effective prevention and intervention.
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1 |
2021 — 2022 |
Sheth, Amit (co-PI) [⬀] Shalin, Valerie Parthasarathy, Srinivasan [⬀] Garrett, R. Hyder, Ayaz |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Nsf Convergence Accelerator Track F: Actionable Sensemaking Tools For Curating and Authenticating Information in the Presence of Misinformation During Crises
High volume, rapidly changing, diverse information, which often includes misinformation, can easily overwhelm decision makers during a crisis. Decisions made both during and long after a crisis, affect the trust between responsible decision makers and citizens (many from vulnerable populations), who are impacted by those decisions. This project seeks to help decision makers manage information, promoting reliance on authentic knowledge production processes while also reducing the impact of intentional disinformation and unintended misinformation. The project team will develop a suite of prototype tools that bring timely, high-quality integrated content to bear on decision making and governance, as a routine part of operations, and especially during a crisis. Integrated and authenticated content comprising scientific facts and technical information coupled with citizen and stakeholder viewpoints assure the accuracy of safety decisions and the appropriate prioritization of relief efforts. The project team will synthesize convergent expertise across multiple disciplines; engage and build stakeholder communities through partnerships with government and industry to guide tool development; build a prototype tool for authenticating data and managing misinformation; and validate the tool using real world crisis scenarios.
The project team will create use-inspired personalized AI-driven sensemaking prototype tools for decision-makers to comprehend and authenticate dynamic, uncertain, and often contradictory information to facilitate effective decisions during crises. The tools will focus on curation while accounting for source and explainable content credibility. Guidance from community stakeholders obtained using ethnographic methods will ensure that the resulting tools are practical, timely, and relevant for informed decision making. These tools will capitalize on features of the information environment, human cognitive abilities and limitations, and algorithmic approaches to managing information. In particular, content and network analyses can reveal constellations of sources with a higher probability of producing credible information, while knowledge graphs can help surface and organize important materials being shared while facilitating explainability. The project team will also design and develop a microworld environment to examine and improve tool robustness while simultaneously helping to train decision makers in real-world settings such as school districts and public health settings. This project represents a convergence of disciplines spanning expertise in computer science, social sciences, linguistics, network science, public health, cognitive science, operations, and communication that are necessary to achieve its goals. Partnerships between communities, government industry, and academia will ensure the deliverables are responsive to stakeholder needs.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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0.979 |