Friday, January 31, 2020

The Interplay Between the Media and the Public in Mass Shootings

Continuing our work on shootings we recently had a paper published in Criminology and Public Policy entitled: "Responses to Mass Shooting Events: The Interplay Between the Media and the Public." However, here we do not look at bots but instead explore the how the public responds to mass shooting events (e.g. Las Vegas, Sutherland Springs, Marshall County, Parkland, Santa Fe), by seeking additional information or exchanging opinions about them in media coverage (e.g. newspaper articles via LexisNexis) and through online sources of information (e.g. Google Trends, Wikipedia and Online Social Networks (i.e. Twitter)). 

Overall, our results show discernible patterns in both time and space in the public’s online information seeking activities after a mass shooting. In addition we find discernible online information seeking patterns in geographic space, with a focal area of interest in the state in which the shooting event occurs, surrounded by a region of reduced interest. This finding further suggests that online information seeking activities are driven, at least in part, by geographic proximity to mass shooting events.

If you wish to find out more about this research, below we provide the summary and policy implication to the paper along with some figures from our methodology (e.g., how we go about analyzing temporal and geographical trends) and some of the results. Finally at the bottom of the post we provide the full reference and a link to the paper.

Research Summary: Public mass shootings tend to capture the public’s attention and receive substantial coverage in both traditional media and online social networks (OSNs) and have become a salient topic in them. Motivated by this, the overarching objective of this paper is to advance our understanding of how the public responds to mass shooting events in such media outlets. Specifically, it aims to examine whether distinct information seeking patterns emerge over time and space, and whether associations between public mass shooting events emerge in online activities and discourse. Towards this objective, we study a sequence of five public mass shooting events that have occurred in the United States between October 2017 and May 2018 across three major dimensions: the public’s online information seeking activities, the media coverage, and the discourse that emerges in a prominent OSN. To capture these dimensions, respectively, data was collected and analyzed from Google Trends, LexisNexis, Wikipedia Page views, and Twitter. The results of our analysis suggest that distinct temporal patterns emerge in the public’s information seeking activities across different platforms, and that associations between an event and its preceding events emerge both in the media coverage and in OSNs.
Policy Implication: Studying the evolution of discourse in OSNs provides a valuable lens to observe how society’s views on public mass shooting events are formed and evolved over time and space. The ability to analyze such data allows tapping into the dynamics of reshaping and reframing public mass shooting events in the public sphere and enable it to be closely studied and modeled. A deeper understanding of this process, along with the emerging associations drawn between such events, can then provide policy and decision-makers with opportunities to better design policies and communicate the significance of their goals and objectives to the public.
A framework for the analysis of temporal and geographical trends .

The analysis processes of Twitter and LexisNexis data.

Geographic patterns in online search activity in Google Trends for the five events in our study.

Chronologically ordered Google Trends search activity (a, left) and Wikipedia page views (b, right). Each vertical solid black line marks the occurrence of one of four shooting events examined in the analysis (as indicated by the line label).

Mentions of prior events during the first approximately 1-month period following each event in each of the events studied. (a) Sutherland Springs, (b) Marshall County, (c) Parkland, (d) Santa Fe.

Full Reference:
Croitoru, A., Kien, S., Mahabir, R., Radzikowski, J., Crooks, A.T., Schuchard, R., Begay, T., Lee, A., Bettios, A. and Stefanidis, A. (2020), Responses to Mass Shooting Events: The Interplay Between the Media and the Public, Criminology and Public Policy, 19(2): 335–360. (pdf)

Thursday, January 30, 2020

Comparison of Emoji Use in Names, Profiles, and Tweets

In most of our work to date with respect to exploring social media, we have only looked at the text or images from online social media platforms (e.g. Twitter and Flickr) and excluded  emojis from the analysis. However, this has now changed with a new paper co-authored with  Melanie Swartz entitled "Comparison of Emoji Use in Names, Profiles, and Tweets" which will be presented at he Eighth IEEE International Workshop on Semantic Computing for Social Networks and Organization Sciences in conjunction with 14th IEEE International Conference on Semantic Computing

In the paper we discuss how emoji use is becoming more and more popular by users of online social networking sites as they can be an effective way to express sentiment, sarcasm or feelings which are not easily conveyed as text. However, limited research has focused on analysis of the behavior of emoji use or how to compare emoji use across users or documents. To overcome this limitation, in this paper: (1) we present a methodology to extract, aggregate, and compare emoji use across a collection of documents based on Unicode emoji category and subcategories, (2) we present a baseline of statistics of emoji use in user names, profile descriptions, and tweets, and (3) we compare emoji use as categories and subcategories between users and content a user shares in the user name, profile description, retweets and non-retweets.

By considering this semantic grouping of emojis, we move the research on emojis beyond just comparing individual emojis and broad aggregations. In applying our methodology to a set of 44 million tweets and over 3 million user profiles, we find that differences in emoji use emerged based on document type (i.e., user names, profile descriptions, retweets, and non-retweets). As such, our work offers a new lens to study and compare forms of self expression across a variety of digital media content types. If you wish to find out more about this work, below we present the abstract to the paper, our workflow that allows for emoji comparison and some results. Finally at the bottom of the page we provide the full reference and a link to the paper.

Online social networking applications are popular venues for self-expression, communication, and building connections between users. One method of expression is that of emojis, which is becoming more prevalent in online social networking platforms. As emoji use has grown over the last decade, differences in emoji usage by individuals and the way they are used in communication is still relatively unknown. This paper fills this gap by comparing emoji use across users and collectively in user names, profiles, and in original and re-shared content. We present a methodology that enables comparison of semantically similar emojis based on Unicode emoji categories and subcategories. We apply this methodology to a corpus of over 44 million tweets and associated user names and profiles to establish a baseline which reveals differences in emoji use in user names, profile descriptions, non-retweets, and retweets. In addition, our analysis reveals emoji super users who have a significantly higher proportion and diversity of emoji use. Our methodology offers a novel approach for summarizing emoji use and enables systematic comparison of emojis across individual user profiles and communication patterns, thus expanding methods for semantic analysis of social media data beyond just text.
Keywords: emoji; social media analytics; content analysis; online social networks.

Workflow for emoji comparison.

Proportion of emoji use in profiles, names, retweets, and non-retweets, ordered by category.

Proportion of emoji use by subcategory.

Top emoji for each communication type.

Swartz, M. and Crooks, A.T. (2020), Comparison of Emoji Use in Names, Profiles, and Tweets, The Eighth IEEE International Workshop on Semantic Computing for Social Networks and Organization Sciences: From User Information to Social Knowledge, San Diego, CA. (pdf)

Tuesday, January 14, 2020

New Paper: Insights into Human-wildlife Interactions in Cities from Bird Sightings Recorded Online

In the past we have explored how social media can be used to delineate earthquakes, locate wildfires or be used to understand urban morphology. However, recently we have also started to explore how social media and crowdsourced data can be utilized to to study socio-environmental systems. Keeping with this them, Bianca Lopez, Emily Minor, and myself have recently had a paper published in Landscape and Urban Planning entitled "Insights into Human-wildlife Interactions in Cities from Bird Sightings Recorded Online."  

In the paper we explore where do people observe birds, using the city of Chicago as our case study. By utilizing urban bird observations collected from eBird, iNaturalist, and Flickr we find that most bird observations occurred in open space zoned for recreational use. Further analysis revealed that the number of bird observations varied with income, population size, and proximity to Lake Michigan. If you want to find out more, below is the abstract to the paper, along with some figures of the results and at the bottom of the post, the full reference and a link to the paper. 

Interactions with nature can improve the wellbeing of urban residents and increase their interest in biodiversity. Many places within cities offer opportunities for people to interact with wildlife, including open space and residential yards and gardens, but little is known about which places within a city people use to observe wildlife. In this study, we used publicly available spatial data on people’s observations of birds from three online platforms—eBird, iNaturalist, and Flickr—to determine where people observe birds within the city of Chicago, Illinois (USA). Specifically, we investigated whether land use or neighborhood demographics explained where people observe birds. We expected that more observations would occur in open spaces, and especially conservation areas, than land uses where people tend to spend more time, but biodiversity is often lower (e.g., residential land). We also expected that more populated neighborhoods and those with higher median age and income of residents would have more bird observations recorded online. We found that bird observations occurred more often in open spaces than in residential areas, with high proportions of observations in recreation areas. In addition, a linear regression model showed that neighborhoods with higher median incomes, those with larger populations, and those located closer to Lake Michigan had more bird observations recorded online. These results have implications for conservation and environmental education efforts in Chicago and demonstrate the potential for social media and citizen science data to provide insight into urban human-wildlife interactions.
Keywords: Urban biodiversity, human-nature interaction, open space, residential, spatial analysis, birdwatching.

Map of bird observations from the three web platforms (Flickr, eBird, and iNaturalist) across the city of Chicago, in relation to mean median income of community areas (left panel) and open space, residential land use, highways, and waterways (right panel).

Proportions of observations recorded in different land uses on the three different online platforms (n = 7944 eBird; n = 474 iNaturalist; n = 561 Flickr). There was a significant difference between the three distributions (simulated p-value less than 0.001), including in the proportions of observations in conservation, recreation, and residential land uses.

Full Reference:
Lopez, B.E., Minor, E.S. and Crooks, A.T. (2020), Insights into Human-wildlife Interactions in Cities from Bird Sightings Recorded Online, Landscape and Urban Planning. 196: 103742. (pdf)

Thursday, January 02, 2020

Models from Teaching CSS Fall 2019

Avid readers of this blog (if there are any) may be familiar with my routine of combing end of semester projects into a short movie and blogging about it. Well its that time again. Last semester I gave a class entitled Introduction to Computational Social Science and instead of setting a final exam, I ask the students to carryout an end of semester research project. The aim of this exercise is to cement what the students have (hopefully) learnt during the semester. I.e.: 
  • to understand the motivation for the use of computational models in social science theory and research; 
  • to learn about the variety of CSS research programs across the social science disciplines; 
  • to understand the distinct contribution that CSS can make by providing specific insights about society, social phenomena at multiple scales, and the nature of social complexity.
Below you can see some of the outputs from these projects this last fall. These models ranged in type from agent-based models, microsimulation to system dynamics models applied to a variety of topics from how machine learning can be utilized within agent-based models to applications such as the courts, common pool resources, public goods, economic growth, supply chains, heath care issues (e.g. patient diagnosis, fungi infections within hospitals), team performance, labor markets, voting, and several other topics along the way.

Thursday, December 19, 2019

New Working Paper: Agent-Based Models for Geographical Systems: A Review

Its been a while since we published a working paper, especially a CASA one, but this has now changed with the release of a new one entitled "Agent-Based Models for Geographical Systems: A Review." In the paper Alison Heppenstall, Nick Malleson, Ed Manley, Jiaqi Ge, Michael Batty and myself reflect back on the agent-based modeling and their use in geographical systems. 

In the paper we revisit challenges that we first explored back in 2008 (which an earlier version was another CASA working paper) and progress that has been made to address them. We then  explore new challenges within the field of agent-based models especially in light of new new (big) data along with new opportunities (such as data assimilation). If you want to find out more about the paper, below is the abstract and a ling to the paper.

This paper charts the progress made since agent-based models (ABMs) of geographical systems emerged from more aggregative approaches to spatial modeling in the early 1990s. We first set the context by noting that ABM explicitly represent the spatial system by individual objects, usually people in the social science domain, with behaviors that we simulate here mainly as decisions about location and movement. Key issues pertaining to the way in which temporal dynamics characterize these models are noted and we then pick up the challenges from the review of this field conducted by Crooks, et al. (2008) some 12 years ago which was also published as a CASA working paper. We then define key issues from this past review as pertaining to a series of questions involving: the rationale for modeling; the way in which theory guides models and vice versa; how models can be compared; questions of model replication,experiment, verification and validation; how dynamics are incorporated in models; how agent behaviors can be simulated; how such ABMs are communicated and disseminated; and finally the data challenges that still dominate the field. This takes us to the current challenges emerging from this discussion. Big data, the way it is generated, and its relevance for ABM is explored with some important caveats as to the relevance of such data for these models, the way these models might be integrated with one another and with different genera of models are noted, while new ways of testing such models through ensemble forecasting and data assimilation are described. The notion about how we model human behaviors through agents learning in complex environment is presented and this then suggests that ABM still have enormous promise for effective simulations of how spatial systems evolve and change.
Full Reference:
Heppenstall, A., Crooks, A.T., Malleson, N., Manley, E., Ge J. and Batty, M. (2019), Agent-Based Models for Geographical Systems: A Review. Centre for Advanced Spatial Analysis (University College London): Working Paper 214, London, England. (pdf)