Soil Moisture Impacts on Convective Precipitation in Oklahoma

Soil moisture is vital to the climate system, as root zone soil moisture has a significant influence on evapotranspiration rates and latent and sensible heat exchange. Through the modification of moisture flux from the land surface to the atmosphere, soil moisture can impact regional temperature and precipitation. Despite a wealth of studies examining land-atmosphere interactions, model and observation-driven studies show conflicting results with regard to the sign and strength of soil moisture feedback to precipitation, particularly in the Southern Great Plains of the United States. This research provides observational evidence for a preferential dry (or negative) soil moisture feedback to precipitation in Oklahoma. The ability of soil moisture to impact the location and occurrence of afternoon convective precipitation is constrained by synoptic-scale atmospheric circulation and resulting mid- and low-level wind patterns and sensible and latent heat flux. Overall, the preference for precipitation initiation over dry soils is enhanced when regional soil moisture gradients exhibit a weakened east to west, wet to dry pattern. Based on these results, we conclude that soil moisture can modify atmospheric conditions potentially leading to convective initiation. However, the land surface feedback signal is weak at best, suggesting that regional-scale circulation is the dominant driver of warm season precipitation in the Southern Great Plains

Investigation of Soil Moisture - Vegetation Interactions in Oklahoma

and-atmosphere interactions are an important component of climate, especially in semi-arid regions such as the Southern Great Plains. Interactions between soil moisture and vegetation modulate land-atmosphere coupling and thus represent a crucial, but not well understood climate factor. This study examines soil moisture-vegetation health interactions using both in situ observations and land surface model simulations. For the observational study, soil moisture is taken from 20 in situ Oklahoma Mesonet soil moisture observation sites, and vegetation health is represented by MODIS-derived normalized difference vegetation index (NDVI). For the modeling study, the variable infiltration capacity (VIC) hydrologic model is employed with two different vegetation parameterizations. The first is the model default vegetation parameter which is interannually-invariant leaf area index (LAI). This parameter is referred to as the control parameter. The second is MODIS-derived LAI, which captures interannual differences in vegetation health. Soil moisture simulations from both vegetation parameterizations are compared and the VIC-simulated soil moisture’s sensitivity to the vegetation parameters is also examined. Correlation results from the observation study suggest that soil moisture-vegetation interactions in Oklahoma are inconsistent, varying both in space and time. The modeling results show that using a vegetation parameterization that does not capture interannual vegetation health variability could potentially result in dry or wet biased soil moisture simulations

Online media literacy intervention in Indonesia reduces misinformation sharing intention

Media literacy is widely viewed as an important tool in the fight against the spread of misinformation online. However, efforts to boost media literacy have primarily focused on Western-media and Western-oriented social media platforms, which are substantively different from the media and platforms used widely in the Global South. In the present work, we focus on the media ecosystem of Indonesia and report the results of an online media literacy intervention consisting of short-videos that were targeted specifically to social media users in Indonesia (N= 656). We found that participants in our media literacy intervention were 64% more likely to reduce their sharing intentions of false headlines than our control group (p \u3c 0.001). Our novel media literacy intervention shows promise as a useful tool to reduce misinformation in Southeast Asia

The Infinity Mirror Test for Graph Models

Graph models, like other machine learning models, have implicit and explicit biases built-in, which often impact performance in nontrivial ways. The model's faithfulness is often measured by comparing the newly generated graph against the source graph using any number or combination of graph properties. Differences in the size or topology of the generated graph therefore indicate a loss in the model. Yet, in many systems, errors encoded in loss functions are subtle and not well understood. In the present work, we introduce the Infinity Mirror test for analyzing the robustness of graph models. This straightforward stress test works by repeatedly fitting a model to its own outputs. A hypothetically perfect graph model would have no deviation from the source graph; however, the model's implicit biases and assumptions are exaggerated by the Infinity Mirror test, exposing potential issues that were previously obscured. Through an analysis of thousands of experiments on synthetic and real-world graphs, we show that several conventional graph models degenerate in exciting and informative ways. We believe that the observed degenerative patterns are clues to the future development of better graph models.Comment: This was submitted to IEEE TKDE 2020, 12 pages and 8 figure

Identifying Bridge Users: the Knowledge Transfer Agents in Enterprise Collaboration Systems

In recent years enterprise collaboration systems (ECS) integrated with social network capabilities have become popular tools for supporting knowledge management (KM) strategies and organizational learning. Increased usage has resulted in higher interest in understanding and classifying the roles that ECS users adopt online. Previous research has investigated user role identification by considering: the degree of participation in an ECS, the user interactions with shared content, the user role in the ECS network, and the user KM-role observed within an interaction. Although all of these factors provide insights into ECS user engagement, they fail to fully consider the knowledge sharing perspective. In this paper, we define bridge users within the context of KM and present a framework for identifying them using semantic analysis of user-generated content. Further, we present results and observations from tests of our pipeline on the ECS of a large multinational engineering company with more than 100k users

MEWS: Real-time Social Media Manipulation Detection and Analysis

This article presents a beta-version of MEWS (Misinformation Early Warning System). It describes the various aspects of the ingestion, manipulation detection, and graphing algorithms employed to determine--in near real-time--the relationships between social media images as they emerge and spread on social media platforms. By combining these various technologies into a single processing pipeline, MEWS can identify manipulated media items as they arise and identify when these particular items begin trending on individual social media platforms or even across multiple platforms. The emergence of a novel manipulation followed by rapid diffusion of the manipulated content suggests a disinformation campaign