Message Quality on Entity Location and Identification Performance

At the recent SPIE Optics + Photonics Conference we had a paper entitled "The Impact of Message Quality on Entity Location and Identification Performance in Distributed Situational Awareness." In the paper we discuss the importance of detecting a person or object in complicated and dynamic environments (such as for search and rescue or law enforcement). However, with the growth in sensors and the resulting information from them, the identification and tracking of objects is becoming more difficult. As a result, in this  paper we provide and assess a method to identify objects and the interactions between agents (human or technological) within a collaborative system for improving situational awareness. Below we provide the abstract to the paper, some images of the system along with some results and a link to the paper.

Abstract 

Location and time are critical to the success of many organizations’ missions. Sensors, software, processors, vehicles, and human analysts work together to accomplish these tasks of detecting and identifying specific entities as quickly as possible for these missions. This work aims to make a contribution by providing a team-based detection and identification performance model incorporating the theory of Distributed Situational Awareness (DSA) and its effect on completing a specific task. The task being the ability to detect and identify a specific entity within a complex urban environment. Conditions to accomplish the task is the utilization of two unmanned aerial vehicles mounted with electro-optical sensors, operated by two analysts, creating a team to execute this task. Our results provide an additional resource on the how technology and training might be utilized to find the best performance given these certain conditions and missions. A highly trained team might improve their performance with this technology, or a team with low training could perform at a high level given the appropriate technology in limited time scenarios. More importantly, the model presented in this paper provides an evaluation tool to compare new technologies and their impact on teams. Specifically, it enables answering questions, such as: is an investment in new technology appropriate if investing in additional training produces the same performance results? Future performance can also be evaluated based on the team’s level of training and use of technology for these specific tasks.

Keywords: Situational Awareness, Identification, Detection, Sensors, Training, Team.

Snapshot from FOCUS depicting the flight paths of the unmanned aerial vehicles (UAVs) and sensor field of view in green.

LiDAR map of the city of Samarra, Iraq utilized in FOCUS for this experiment.

Identification of Situational Awareness (SA) level data sets - baseline. Histogram and distribution curve.

Full Reference:

Bates, C.T., Croitoru, A., Crooks, A.T. and Harclerode, E. (2019), The Impact of Message Quality on Entity Location and Identification Performance in Distributed Situational Awareness, Proceedings of the SPIE Optics + Photonics, San Diego, CA. Paper 11137-64 (pdf)

Call for Papers: Humans, Societies and Artificial Agents - SpringSim

At the upcoming 2020 Spring Simulation (SpringSim) Conference being held at George Mason University, Philippe Giabbanelli and myslef are organizing a tract entitled Humans, Societies and Artificial Agents. 

Aims and Scope:

In the Humans, Societies and Artificial Agents (HSAA) Track, you will see that artificial societies have typically relied on agent-based models, Geographical Information Systems (GIS), or cellular automata to capture the decision-making processes of individuals in relation to places and/or social interactions. This has supported a wide range of applications (e.g., in archaeology, economics, geography, psychology, political science, or health) and research tasks (e.g., what-if scenarios or predictive models, models to guide data collection). Several opportunities have recently emerged that augment the capacity of artificial societies at capturing complex human and social behavior. Mixed-methods and hybrid approaches now enable the use of ‘big data’, for instance by combining machine learning with artificial societies to explore the model’s output (i.e., artificial societies as input to machine learning), define the model structure (i.e. machine learning as a preliminary to designing artificial societies), or run a model efficiently (i.e. machine learning as a proxy or surrogate to artificial societies). Datasets are also broader in type since artificial societies can now be built from text, or generate textual as well as visual outputs to better engage end-users. Authors are encouraged to submit papers in the following areas:

• Artificial agents and societies (e.g., case studies, analyses of moral and ethical considerations)
• Participatory modeling and simulation
• Policy development and evaluation through simulations
• Predictive models of social behavior
• Simulations of societies as public educational tools
• Mixed-methods (e.g., analyzing or generating text data with artificial societies, combining machine learning and artificial societies)
• Models of individual decision-making, mobility patterns, or socio-environmental interactions
• Testbeds and environments to facilitate artificial society development
• Tools and methods (e.g., agent-based models, case-based modeling, soft systems)

Important Dates

• Paper Submission: December 16, 2019 Extended to January 6, 2020
• Author Notification: February 17, 2020 
• Camera-Ready: March 4, 202

For more information, including submission guidelines can be found at https://scs.org/springsim/.  

Bot stamina: Examining the influence and staying power of bots in online social networks

Following on with our work on bots we just had a paper published in Applied Network Science entitled "Bot Stamina: Examining the Influence and Staying Power of Bots in Online Social Networks" which is significant extension of a previous conference paper. In the paper we look at thee global Twitter conversions in 2016. Specifically the 2016 U.S. presidential election primary races (February 1–28, 2016), the ongoing Ukrainian conflict (August 1–28, 2016), and the Turkish government’s implementation of censorship (December 1–28, 2016) and the influence of Bots on these conversations.

Tweets were classified as Bots using DeBot and then we explore the relative importance and persistence of social bots in online social networks by looking at retweet networks and centrality rankings (i.e. degree, in-degree, out-degree, eigenvector, betweenness and PageRank). We find through such centrality measurements that even though Bots made up less than 0.3% of the total user population, they displayed a profound level of structural network influence.  If you would like to know more about this work, below we provide the abstract to the paper, along with some figures, including one that describes our methodology, and some initial results. Finally at the bottom of the page we provide the full reference and a link to the paper.

Abstract

This study presents a novel approach to expand the emergent area of social bot research. We employ a methodological framework that aggregates and fuses data from multiple global Twitter conversations with available bot detection platforms and ultimately classifies the relative importance and persistence of social bots in online social networks (OSNs). In testing this methodology across three major global event OSN conversations in 2016, we confirmed the hyper-social nature of bots: suspected social bot accounts make far more attempts on average than social media accounts attributed to human users to initiate contact with other accounts via re-tweets. Social network analysis centrality measurements discover that social bots, while comprising less than 0.3% of the total corpus user population, display a dis-proportionately high profound level of structural network influence by ranking particularly high among the top users across multiple centrality measures within the OSN conversations of interest. Further, we show that social bots exhibit temporal persistence in centrality ranking density when examining these same OSN conversations over time.

Keywords: Social bot analysis, computational social science, social network analysis, online social networks

Full Reference:

Schuchard, R., Crooks, A.T., Stefanidis, A. and Croitoru, A. (2019), Bot Stamina: Examining the Influence and Staying Power of Bots in Online Social Networks, Applied Network Science, 4: 55. Available at https://doi.org/10.1007/s41109-019-0164-x. (pdf)

Location-Based Social Simulation

At the upcoming 16th International Symposium on Spatial and Temporal Databases (SSTD) we have vision paper entitled "Location-Based Social Simulation" accepted. In the paper we discuss issues such as data sparsity and privacy concerns with using real world location-based social networks (LBSNs) like Foursquare and Yelp. To overcomes these issues, we describe how one can employ geospatial simulation (i.e. an agent-based model) to create artificial, but socially plausible LBSN data sets which overcomes some of the limitations with respect to LBSNs.

ABSTRACT:

Location-based social networks (LBSNs) have been studied extensively in recent years. However, utilizing real-world LBSN datasets in such studies has severe weaknesses: sparse and small datasets, privacy concerns, and a lack of authoritative ground-truth. Our vision is to create a large scale geosimulation framework to simulate human behavior and to create synthetic but realistic LBSN data that captures the location of users over time as well as social interactions of users in a social network. While existing LBSN datasets are trivially small, such a framework would provide the first source of very large LBSN benchmark data which would closely mimic the real world, containing high-fidelity information of location, and social connections of millions of simulated agents over several years of simulated time. Therefore, it would serve the research community by revitalizing and reshaping research on LBSNs by allowing researchers to see the (simulated) world through the lens of an omniscient entity having perfect data. These evaluations will guide future research allowing us to develop solutions to improve LBSN applications such as user-location recommendation, friend recommendation, location prediction, and location privacy.

KEYWORDS: Agent-based simulation, location-based social network, data generator, spatial network, human behavior

Full Reference: 

Kavak, H., Kim, J-S., Crooks, A.T., Pfoser, D., Wenk C. and Züfle, A (2019), Location-Based Social Simulation, Proceedings of the 16th International Symposium on Spatial and Temporal Databases, Vienna, Austria, pp 218-221. (pdf)

Update: Our paper was selected as runner-up  for best Vision Paper.

Challenges and Opportunities of Social Media Data for Socio-environmental Systems Research

SES diagram with examples of topics that
have been researched using social media data

While I have written about how one can use social media data to study cities, health issues etc... more recently we have been looking into how such data can be used to aid  Socio-environmental Systems (SES) research. SES are defined as tightly linked social and biophysical subsystems that mutually influence one another through positive and negative feedbacks.  To this end, Bianca Lopez, Nick Magliocca and myself just ahd a paper published in Land entitled "Challenges and Opportunities of Social Media Data for Socio-environmental Systems Research." 

In this paper we discuss SES and how research into them poses many challenges, not least of which are collecting or compiling data at the appropriate scales and aligning social and environmental data to address SES questions.  We discuss how SES have been studied using more traditional sources of data (e.g. census data, remote sensing etc.) and explore how social media can be used in the context of SES research. Specifically we ask three specific questions. 1) How can feedback between social and environmental systems be meaningfully studied using social media data? 2) How can using social media data re-frame or compliment current SES research questions and methods? and 3) Are there best practices for collecting and validating social media data for use in SES research? If these questions sound interesting to you, we encourage you to read the abstract below or the full paper.

Abstract:

Social media data provide an unprecedented wealth of information on people’s perceptions, attitudes, and behaviors at fine spatial and temporal scales and over broad extents. Social media data produce insight into relationships between people and the environment at scales that are generally prohibited by the spatial and temporal mismatch between traditional social and environmental data. These data thus have great potential for use in socio-environmental systems (SES) research. However, biases in who uses social media platforms and what they use them for create uncertainty in the potential insights from these data. Here, we describe ways that social media data have been used in SES research, including tracking land-use and environmental changes, natural resource use, and ecosystem service provisioning. We also highlight promising areas for future research and present best practices for SES research using social media data.

Keywords: social media; socio-ecological systems; human-environment interactions; geospatial analysis; crowdsourced data.

Example of information provided by social media posts and how it is used in analyses. A single post from a social media user.

Example of information on people’s use of natural resources from social media data based on key word searches fish and oyster from Twitter, Instagram and Foursquare.

Full Reference:

Lopez, B., Magliocca, N. and Crooks, A.T. (2019), Challenges and Opportunities of Social Media Data for Socio-environmental Systems Research, Land, 8(7), 107; https://www.mdpi.com/2073-445X/8/7/107/htm  (pdf)

 

Update: This review paper was awarded second prize for Best Review Paper Award in Land 2019.