Thursday, August 01, 2019

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)

Friday, July 26, 2019

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 Z├╝fle, A (2019), Location-Based Social Simulation, 16th International Symposium on Spatial and Temporal Databases, Vienna, Austria. (pdf)

Thursday, July 04, 2019

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)

Monday, July 01, 2019

Modeling Society Reacting to a Nuclear Weapon of Mass Destruction Event

https://www.dropbox.com/s/mid39pfgvu1vr8l/SBPBrims_2019_Poster.pdf?dl=0
Over the last couple of years we have been working on generating synthetic human populations with realistic social networks with respect to the New York mega-city and surrounding region. This is being done for a variety of modeling applications such as the spread of a disease or exploring peoples reactions to disasters (which was a topic of a recent post on Computational Social Science of Disasters).

To this end, at the upcoming International Conference on Social Computing, Behavioral-Cultural Modeling and; Prediction and Behavior Representation in Modeling and Simulation (or SBP-BRiMS for short) we have a short working paper outlining some of our initial efforts to how people might react following a Nuclear Weapon of Mass Destruction (NWMD) event. In the paper we show some preliminary simulation results relating on  how we are able to simulate basic commuting patterns and initial movement away from the affected area after the NWMD event (like those in the movies below). By using a synthetic population we are able to create an artificial world populated by agents with sufficient heterogeneity to create realistic movement patterns and the social networks which play a vital role in disaster situations. If you want to know more about this work, feel free to read the abstract blow or read the paper. 

Abstract:
Individual connections between human beings often dictate where people go and how they behave, yet their representation through social networks are rarely used as measures of human behavior in agent-based models. Social networks are increasingly used for study of human behavior in disasters, and empirical work has shown that human beings prioritize the safety of themselves and loved ones (i.e., households) before helping neighbors and coworkers. Based on this assumption we have created a set of heuristics for modeling how agents behave in an emergency event and how the individual behavior aggregates into a variety of patterns of life. In this paper will present briefly our agent-based model being used to characterize the population’s reaction to a Nuclear Weapon of Mass Destruction (NWMD) event in the New York City region. Agents are modeled commuting on work-day schedules before the explosion of a small (10Kt) nuclear device. After the explosion, agents respond to signals in their environment and make decisions based on prioritization of safety for themselves and those in their networks. The model methodology demonstrates how social networks can be integrated into an agent-based model and act as a basis for decision-making, and preliminary simulations show how agents potentially respond to a NWMD event with measurable changes in location and network formations over space and time. 

Keywords: Agent-Based Model, Human Behavior, Social Networks, Emergency, Disaster Response, Nuclear Weapon of Mass Destruction.
Various patterns of commuting behavior representing daily routines of the individual agents.





Full Reference:
Burger, A. G., Kennedy, W.G., Crooks, A.T., Jiang, N. and Guillen-Piazza, D. (2019), Modeling Society Reacting to a Nuclear Weapon of Mass Destruction Event, 2019 International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, Washington DC. (pdf)

Saturday, June 08, 2019

A Semester of CSS 645: Spatial Agent-based Models of Human-Environment Interactions

This last Spring semester I taught a class entitled "Spatial Agent-based Models of Human-Environment Interactions". As with many of my courses, students were expected to complete a end of semester project, in this case, develop an agent-based model that explores some aspect of related to the course theme of human-environment interactions.  For several of the students this was their first exposure to either agent-based modeling or utilizing geographical information in the modeling process. In the movie below a selection of these projects can be seen. The projects ranged from migration, evacuation modeling during a natural disaster, gerrymandering, the spread of diseases, recidivism, Commons problems to that of urban decline. As can be seen the models ranged from abstract spatial representations to those utilizing geographical information as a foundation of their artificial worlds. Many of the models where created using NetLogo.



I would like to thank the Students of CSS 645: Spatial Agent-based Models of Human-Environment Interactions for their participation in the class.