publications

2024

1. Examining Similar and Ideologically Correlated Imagery in Online Political Communication

   Amogh Joshi, and Cody Buntain

   Proceedings of the International AAAI Conference on Web and Social Media, 2024

   Abs

   This paper investigates visual media shared by US national politicians on Twitter, how a politician’s variety of image types shared reflects their political position, and identifies a hazard in using standard methods for image characterization in this context. While past work has yielded valuable results on politicians’ use of imagery in social media, that work has focused primarily on photographic media, which may be insufficient given the variety of visual media shared in such spaces (e.g., infographics, illustrations, or memes). Leveraging multiple popular, pre-trained, deep-learning models to characterize politicians’ visuals, this work uses clustering to identify eight types of visual media shared on Twitter, several of which are not photographic in nature. Results show individual politicians share a variety of these types, and the distributions of their imagery across these clusters is correlated with their overall ideological position – e.g., liberal politicians appear to share a larger proportion of infographic-style images, and conservative politicians appear to share more patriotic imagery. Manual assessment, however, reveals that these image-characterization models often group visually similar images with different semantic meaning into the same clusters, which has implications for how researchers interpret clusters in this space and cluster-based correlations with political ideology. In particular, collapsing semantic meaning in these pre-trained models may drive null findings on certain clusters of images rather than politicians across the ideological spectrum sharing common types of imagery. We end this paper with a set of researcher recommendations to prevent such issues.

2023

1. Social media emotions annotation guide (SMEmo): Development and initial validity

   Susannah B. F. Paletz, Ewa M. Golonka, Nick B. Pandža, and 8 more authors

   Behavior Research Methods, 2023

   Abs

   The proper measurement of emotion is vital to understanding the relationship between emotional expression in social media and other factors, such as online information sharing. This work develops a standardized annotation scheme for quantifying emotions in social media using recent emotion theory and research. Human annotators assessed both social media posts and their own reactions to the posts’ content on scales of 0 to 100 for each of 20 (Study 1) and 23 (Study 2) emotions. For Study 1, we analyzed English-language posts from Twitter (N = 244) and YouTube (N = 50). Associations between emotion ratings and text-based measures (LIWC, VADER, EmoLex, NRC-EIL, Emotionality) demonstrated convergent and discriminant validity. In Study 2, we tested an expanded version of the scheme in-country, in-language, on Polish (N = 3648) and Lithuanian (N = 1934) multimedia Facebook posts. While the correlations were lower than with English, patterns of convergent and discriminant validity with EmoLex and NRC-EIL still held. Coder reliability was strong across samples, with intraclass correlations of .80 or higher for 10 different emotions in Study 1 and 16 different emotions in Study 2. This research improves the measurement of emotions in social media to include more dimensions, multimedia, and context compared to prior schemes.

2. Cross-Platform Reactions to the Post-January 6 Deplatforming

   Cody Buntain, Martin Innes, Tamar Mitts, and 1 more author

   Journal of Quantitative Description: Digital Media, 2023

   Abs

   We study changes in social media usage following the ‘Great Deplatforming’ inthe aftermath of the 6 January 2021 attack on the US Capitol. Following theattack, several major platforms banned thousands of accounts, ostensibly tolimit misinformation about voter fraud and suppress calls for violence. At thesame time, alternative platforms like Gab, BitChute, and Parler welcomed thesedeplatformed individuals. We identify three key patterns: First, in studyingthe platforms that emerged among users seeking alternative spaces, we see highfrequencies of users bridging these communities announcing their intent to joinnon-mainstream platforms to their audiences on mainstream platforms. Second, focusing on platforms that were created to be alternative, anti-censorshipspaces, deplatforming preceded a sustained increase in engagement with Gabacross Twitter, Reddit, and Google search, while Parler saw a steep decline inengagement. Third, examining the language in these spaces, toxic discourseincreased briefly on Reddit and Twitter but returned to normal after the deplatforming, while Gab became more toxic. These results suggest that whiledeplatforming may precede a reduction in targeted discussions within a specific platform, it can incentivize users to seek alternative platforms where thesediscussions are less regulated and often more extreme.As these alternative spaces are often more political and extreme than their mainstream counterparts, deplatforming may drive single-platform improvements at the expense of the larger information ecosystem.

3. Emotional content and sharing on Facebook: A theory cage match

   Susannah B. F. Paletz, Michael A. Johns, Egle E. Murauskaite, and 5 more authors

   Science Advances, 2023

   Abs

   While emotional content predicts social media post sharing, competing theories of emotion imply different predictions about how emotional content will influence the virality of social media posts. We tested and compared these theoretical frameworks. Teams of annotators assessed more than 4000 multimedia posts from Polish and Lithuanian Facebook for more than 20 emotions. We found that, drawing on semantic space theory, modeling discrete emotions independently was superior to models examining valence (positive or negative), activation/arousal (high or low), or clusters of emotions and was on par with but had more explanatory power than a seven basic emotion model. Certain discrete emotions were associated with post sharing, including both positive and negative and relatively lower and higher activation/arousal emotions (e.g., amusement, cute/kama muta, anger, and sadness) even when controlling for number of followers, time up, topic, and Facebook angry reactions. These results provide key insights into better understanding of social media post virality.

4. Characterizing Image Sharing Behaviors in US Politically Engaged, Random, and Demographic Audience Segments

   Keng-Chi Chang, and Cody Buntain

   In Workshop Proceedings of the 17th International AAAI Conference on Web and Social Media, Jun 2023
5. Measuring the Ideology of Audiences for Web Links and Domains Using Differentially Private Engagement Data

   Cody Buntain, Richard Bonneau, Jonathan Nagler, and 1 more author

   Proceedings of the International AAAI Conference on Web and Social Media, Jun 2023

   Abs

   This paper demonstrates the use of differentially private hyperlink-level engagement data for measuring ideologies of audiences for web domains, individual links, or aggregations thereof. We examine a simple metric for measuring this ideological position and assess the conditions under which the metric is robust to injected, privacy-preserving noise. This assessment provides insights into and constraints on the level of activity one should observe when applying this metric to privacy-protected data. Grounding this work is a massive dataset of social media engagement activity where privacy-preserving noise has been injected into the activity data, provided by Facebook and the Social Science One (SS1) consortium. Using this dataset, we validate our ideology measures by comparing to similar, published work on sharing-based, homophily- and content-oriented measures, where we show consistently high correlation (>0.87). We then apply this metric to individual links from several popular news domains and demonstrate how one can assess link-level distributions of ideological audiences. We further show this estimator is robust to selection of engagement types besides sharing, where domain-level audience-ideology assessments based on views and likes show no significant difference compared to sharing-based estimates. Estimates of partisanship, however, suggest the viewing audience is more moderate than the audiences who share and like these domains. Beyond providing thresholds on sufficient activity for measuring audience ideology and comparing three types of engagement, this analysis provides a blueprint for ensuring robustness of future work to differential privacy protections.

2022

1. Ideological Trajectories in 
Recommendation Systems for News Consumption

   Cody Buntain

   In Conference on Political and Computational Social Science, Jun 2022
2. An Imperative to Assess Socio-Technical Impact of Algorithms in Online Spaces and Wresting Responsibility from Technology Companies

   Cody Buntain

   2022 IEEE 8th International Conference on Collaboration and Internet Computing (CIC), Jun 2022

   Abs

   Today’s information ecosystem has a plethora of tools to support decision-making and cognitive offloading. As this ecosystem has evolved and grown, however, we increasingly rely on recommendation systems, algorithmic curation, and other technologies to make decisions on our behalf and help manage an otherwise-overwhelming information environment. This cognitive offloading comes at a price, as these technologies make decisions that govern the information sources to which we are exposed, the celebrities we are likely follow, the content likely to become popular, the order of information shown to us, and even the state of our emotions. Despite the potential effects of this tradeoff, the true socio-technical impact of this reliance and imbuing these technologies with so much influence over the information space remains both an open and a controversial question. This paper outlines different forms of this question, wherein I summarize the controversies surrounding these technologies and the mixed evidence from academic research on them. I then describe the barriers impeding resolution of these concerns and how the current trajectories of online social platforms and the technology companies that own them are unlikely to provide answers to these issues without external intervention. I close by describing a "socio-technical safety triad" for online social spaces, where the responsibilities and incentives for reducing societal harm are devolved from corporations and spread across academic, governmental, and corporate stakeholders.

3. Incident Streams 2021 off the Deep End: Deeper Annotations and Evaluations in Twitter

   Cody Buntain, Richard McCreadie, and Ian Soboroff

   In ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management, Jun 2022

   Abs

   This paper summarizes the final year of the four-year Incident Streams track (TREC-IS), which has produced a large dataset comprising 136,263 annotated tweets, spanning 98 crisis events. Goals of this final year were twofold: 1) to add new categories for assessing messages, with a focus on characterizing the audience, author, and images associated with these messages, and 2) to significantly enlarge the TREC-IS dataset with new events, with an emphasis of deeper pools for sampling. Beyond these two goals, TREC-IS has nearly doubled the number of annotated messages per event for the 26 crises introduced in 2021 and has released a new parallel dataset of 312,546 images associated with crisis content – with 7,297 tweets having annotations about their embedded images. Our analyses of this new crisis data yields new insights about the context of a tweet; e.g., messages intended for a local audience and those that contain images of weather forecasts and infographics have higher than average assessments of priority but are relatively rare. Tweets containing images, however, have significantly higher perceived priorities than tweets without images. Moving to deeper pools, while tending to lower classification performance, also does not generally impact performance rankings or alter distributions of information-types. We end this paper with a discussion of these datasets, analyses, their implications, and how they contribute both new data and insights to the broader crisis informatics community.

4. Characterizing YouTube and BitChute Content and Mobilizers During U.S. Election Fraud Discussions on Twitter

   Matthew Childs, Cody Buntain, Milo Z. Trujillo, and 1 more author

   14th ACM Web Science Conference 2022, Jun 2022

   Abs

   In this study, we characterize the cross-platform mobilization of YouTube and BitChute videos on Twitter during the 2020 U.S. Election fraud discussions. Specifically, we extend the VoterFraud2020 dataset [1] to describe the prevalence of content supplied by both platforms, the mobilizers of that content, the suppliers of that content, and the content itself. We find that while BitChute videos promoting election fraud claims were linked to and engaged with in the Twitter discussion, they played a relatively small role compared to YouTube videos promoting fraud claims. This core finding points to the continued need for proactive, consistent, and collaborative content moderation solutions rather than the reactive and inconsistent solutions currently being used. Additionally, we find that cross-platform disinformation spread from video platforms was not prominently from bot accounts or political elites, but rather average Twitter users. This finding supports past work arguing that research on disinformation should move beyond a focus on bots and trolls to a focus on participatory disinformation spread.

5. Powered by Twitter - The Taliban’s Takeover of Afghanistan

   Laura Courchesne, Bahar Rasikh, Brian McQuinn, and 1 more author

   Oct 2022
6. Exploiting the Right: Inferring Ideological Alignment in Online Influence Campaigns Using Shared Images

   Amogh Joshi, and Cody Buntain

   arXiv, Oct 2022

   Abs

   This work advances investigations into the visual media shared by agents in disinformation campaigns by characterizing the images shared by accounts identified by Twitter as being part of such campaigns. Using images shared by US politicians’ Twitter accounts as a baseline and training set, we build models for inferring the ideological presentation of accounts using the images they share. Results show that, while our models recover the expected bimodal ideological distribution of US politicians, we find that, on average, four separate influence campaigns – attributed to Iran, Russia, China, and Venezuela – all present conservative ideological presentations in the images they share. Given that prior work has shown Twitter accounts used by Russian disinformation agents are ideologically diverse in the text and news they share, these image-oriented findings provide new insights into potential axes of coordination and suggest these accounts may not present consistent ideological positions across modalities.

7. Paved with Bad Intentions: QAnon’s Save the Children Campaign

   Cody Buntain, Monique Deal Barlow, Mia Bloom, and 1 more author

   Journal of Online Trust and Safety, Oct 2022

   Abs

   In the lead-up to the 2020 US presidential election, adherents to QAnon conspiracy theories parlayed the “Save the Children” slogan from a well-known charity into a vector for exposure and political mobilization. This paper introduces three factors that potentially contributed space for appropriating the slogan into the QAnon-backed #SaveTheChildren campaign and, in this context, examines images of children shown alongside QAnon #SaveTheChildren messages. For these factors, we examine the use of race, age, gender, and graphic depictions in this exploitative imagery. Results show QAnon-related images massively over-represent preteen, white children compared to child-trafficking statistics and include a substantial proportion of graphic imagery. Building on studies of child-advocacy campaigns, we contend that QAnon imagery uses the familiar motif of distressed children while presenting a narrative distinct from both these child-advocacy groups and the realities of child trafficking. Embedded in broader contexts of race, gender, and emotional manipulation in online spaces, we discuss how these images are particularly impactful for conservative, white, and especially female audiences in the US and how other movements might be at similar risk of appropriation. The paper then closes by describing possible interventions to protect the safety of online audiences.

8. Exploiting the Right: Inferring Ideological Alignment in Online Influence Campaigns Using Shared Images

   Amogh Joshi, and Cody Buntain

   In Workshop Proceedings of the 16th International AAAI Conference on Web and Social Media, Jun 2022

   Abs

   This work advances investigations into the visual media shared by agents in disinformation campaigns by characterizing the images shared by accounts identified by Twitter as being part of such campaigns. Using images shared by US politicians’ Twitter accounts as a baseline and training set, we build models for inferring the ideological presentation of accounts using the images they share. Results show that, while our models recover the expected bimodal ideological distribution of US politicians, we find that, on average, four separate influence campaigns – attributed to Iran, Russia, China, and Venezuela – all present conservative ideological presentations in the images they share. Given that prior work has shown Twitter accounts used by Russian disinformation agents are ideologically diverse in the text and news they share, these image-oriented findings provide new insights into potential axes of coordination and suggest these accounts may not present consistent ideological positions across modalities.

2021

1. Incident Streams 2020 : TRECIS in the Time of COVID-19

   Cody Buntain, Cody Buntain, Richard Mccreadie, and 1 more author

   18th International Conference on Information Systems for Crisis Response and Management, Jun 2021
2. Social Media for Emergency Management : Opportunities and Challenges at the Intersection of Research and Practice

   Carlos Castillo, Steve Peterson, Paola Rufolo, and 3 more authors

   In 18th International Conference on Information Systems for Crisis Response and Management, Jun 2021
3. Topics of COVID-19 Vaccines and Categories of Misinformation

   Cody Buntain, and Megan Resurreccion

   In North American Social Networking Conference, Data Challenge, Jun 2021

2020

1. Cross-Platform State Propaganda: Russian Trolls on Twitter and YouTube during the 2016 U.S. Presidential Election

   Yevgeniy Golovchenko, Cody Buntain, Gregory Eady, and 2 more authors

   The International Journal of Press/Politics, Jun 2020

   Abs

   This paper investigates online propaganda strategies of the Internet Research Agency (IRA)—Russian “trolls”—during the 2016 U.S. presidential election. We assess claims that the IRA sought either to (1) support Donald Trump or (2) sow discord among the U.S. public by analyzing hyperlinks contained in 108,781 IRA tweets. Our results show that although IRA accounts promoted links to both sides of the ideological spectrum, “conservative” trolls were more active than “liberal” ones. The IRA also shared content across social media platforms, particularly YouTube—the second-most linked destination among IRA tweets. Although overall news content shared by trolls leaned moderate to conservative, we find troll accounts on both sides of the ideological spectrum, and these accounts maintain their political alignment. Links to YouTube videos were decidedly conservative, however. While mixed, this evidence is consistent with the IRA’s supporting the Republican campaign, but the IRA’s strategy was multifaceted, with an ideological division of labor among accounts. We contextualize these results as consistent with a pre-propaganda strategy. This work demonstrates the need to view political communication in the context of the broader media ecology, as governments exploit the interconnected information ecosystem to pursue covert propaganda strategies.

2. Information Processing on Social Media Networks as Emergent Collective Intelligence

   Martin Smyth, Cody Buntain, Debra Dwyer, and 4 more authors

   ACM Collective Intelligence 2020, Jun 2020
3. What is BitChute? Characterizing the "Free Speech" Alternative to YouTube

   Milo Trujillo, Maur\’\icio Gruppi, Cody Buntain, and 1 more author

   In Proceedings of the 31st ACM Conference on Hypertext and Social Media, Jun 2020

   Abs

   In this paper, we characterize the content and discourse on BitChute, a social video-hosting platform. Launched in 2017 as an alternative to YouTube, BitChute joins an ecosystem of alternative, low content moderation platforms, including Gab, Voat, Minds, and 4chan. Uniquely, BitChute is the first of these alternative platforms to focus on video content and is growing in popularity. Our analysis reveals several key characteristics of the platform. We find that only a handful of channels receive any engagement, and almost all of those channels contain conspiracies or hate speech. This high rate of hate speech on the platform as a whole, much of which is anti-Semitic, is particularly concerning. Our results suggest that BitChute has a higher rate of hate speech than Gab but less than 4chan. Lastly, we find that while some BitChute content producers have been banned from other platforms, many maintain profiles on mainstream social media platforms, particularly YouTube. This paper contributes a first look at the content and discourse on BitChute and provides a building block for future research on low content moderation platforms.

4. Incident Streams 2019 : Actionable Insights and How to Find Them

   Richard McCreadie, Cody Buntain, and Ian Soboroff

   In Proceedings of the 17th International Conference on Information Systems for Crisis Response And Management, Jun 2020
5. Hawkes binomial topic model with applications to coupled conflict-Twitter data

   George Mohler, Erin McGrath, Cody Buntain, and 1 more author

   The Annals of Applied Statistics, Jun 2020
6. Artificial Dissimilarity : Multi-Modal Content Similarities in Online Disinformation Campaigns

   Cody Buntain, Vishakh Padmakumar, Richard Bonneau, and 2 more authors

   In ACM Collective Intelligence 2020, Jun 2020
7. Cross-Platform State Propaganda: Russian Trolls on Twitter and YouTube During the 2016 US Presidential Election

   Yevgenii Yevgeniy Golovchenko, Cody Buntain, Gregory Eady, and 6 more authors

   International Journal of Press/Politics, Jun 2020

   Abs

   This paper investigates online propaganda strategies of the Internet Research Agency (IRA)—Russian “trolls”—during the 2016 U.S. presidential election. We assess claims that the IRA sought either to (1) support Donald Trump or (2) sow discord among the U.S. public by analyzing hyperlinks contained in 108,781 IRA tweets. Our results show that although IRA accounts promoted links to both sides of the ideological spectrum, “conservative” trolls were more active than “liberal” ones. The IRA also shared content across social media platforms, particularly YouTube—the second-most linked destination among IRA tweets. Although overall news content shared by trolls leaned moderate to conservative, we find troll accounts on both sides of the ideological spectrum, and these accounts maintain their political alignment. Links to YouTube videos were decidedly conservative, however. While mixed, this evidence is consistent with the IRA’s supporting the Republican campaign, but the IRA’s strategy was multifaceted, with an ideological division of labor among accounts. We contextualize these results as consistent with a pre-propaganda strategy. This work demonstrates the need to view political communication in the context of the broader media ecology, as governments exploit the interconnected information ecosystem to pursue covert propaganda strategies.

8. What is BitChute? Characterizing the "Free Speech" Alternative to YouTube

   Milo Trujillo, Maurício Gruppi, Cody Buntain, and 1 more author

   Jun 2020

   Abs

   In this paper, we characterize the content and discourse on BitChute, a social video-hosting platform. Launched in 2017 as an alternative to YouTube, BitChute joins an ecosystem of alternative, low content moderation platforms, including Gab, Voat, Minds, and 4chan. Uniquely, BitChute is the first of these alternative platforms to focus on video content and is growing in popularity. Our analysis reveals several key characteristics of the platform. We find that only a handful of channels receive any engagement, and almost all of those channels contain conspiracies or hate speech. This high rate of hate speech on the platform as a whole, much of which is anti-Semitic, is particularly concerning. Our results suggest that BitChute has a higher rate of hate speech than Gab but less than 4chan. Lastly, we find that while some BitChute content producers have been banned from other platforms, many maintain profiles on mainstream social media platforms, particularly YouTube. This paper contributes a first look at the content and discourse on BitChute and provides a building block for future research on low content moderation platforms.

9. Content-Based Features Predict Social Media Influence Operations

   Meysam Alizadeh, Jacob Shapiro, Cody Buntain, and 1 more author

   Science Advances, Jun 2020

2019

1. #Handsoffmyada: A twitter response to the ada education and reform act

   Brooke E. Auxier, Cody L. Buntain, Paul Jaeger, and 2 more authors

   Conference on Human Factors in Computing Systems - Proceedings, Jun 2019

   Abs

   Twitter continues to be used increasingly for communication related advocacy, activism, and social change. This is also the case for the disability community. In light of the recently proposed ADA Education and Reform in the United States, we investigate factors for effectiveness of sharing or retweeting messages about topics affecting the rights of people with disabilities. We perform a multifaceted study of the #HandsOffMyADA campaign against the proposed H.R.620 bill to: (1) explore how communication via Twitter compares to previous disability rights movements; (2) characterize the campaign in terms of hashtags, user groups, and content such as accessible multimedia that contribute to dissemination of campaign messages; (3) identify major themes in tweets and responses, and their variation among user groups; and (4) understand how the disability community mobilized for this campaign compared to previous Twitter initiatives.

2. Analyzing a fake news authorship network

   Cody Buntain, Jennifer Golbeck, Brooke Auxier, and 33 more authors

   In iConference 2019 Proceedings, Jun 2019
3. TREC Incident Streams: Finding Actionable Information on Social Media

   Richard Mccreadie, Cody Buntain, and Ian Soboroff

   In Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management, Jun 2019

   Abs

   The Text Retrieval Conference (TREC) Incident Streams track is a new initiative that aims to mature social media-based emergency response technology. This initiative advances the state of the art in this area through an evaluation challenge, which attracts researchers and developers from across the globe. The 2018 edition of the track provides a standardized evaluation methodology, an ontology of emergency-relevant social media information types, proposes a scale for information criticality, and releases a dataset containing fifteen test events and approximately 20,000 labeled tweets. Analysis of this dataset reveals a significant amount of actionable information on social media during emergencies (> 10%). While this data is valuable for emergency response efforts, analysis of the 39 state-of-the-art systems demonstrate a performance gap in identifying this data. We therefore find the current state-of-the-art is insufficient for emergency responders’ requirements, particularly for rare actionable information for which there is little prior training data available.

4. Towards a General Understanding of Coordinated Action Across Online Social Platforms: A Case Study on Russian Manipulation

   Cody Buntain, Fridolin Linder, Richard Bonneau, and 2 more authors

   Jun 2019

2018

1. Characterizing Gender Differences in Misogynistic and Antisocial Microblog Posts

   Cody Buntain

   Jun 2018

   Abs

   This chapter presents an observational study into the genders of authors posting abusive misogynistic insults and hate speech on Twitter. We first characterize the different uses of potentially abusive and misogynistic expletives in Twitter using a novel diversity-based sampling strategy and use Amazon’s Mechanical Turk (MTurk) crowdsourcing platform to construct a labeled dataset of abusive, misogynistic insults. This misogyny dataset and datasets from prior work on harassing and hate speech in Twitter provide datasets for evaluating classification algorithms that can automatically identify this antisocial content. Leveraging 1.8 billion tweets from Twitter’s 1% public sample stream between 1 January 2015 to 31 December 2015, results show this antisocial content is relatively rare on Twitter, accounting for less than 1% of English tweets. After applying a gender classifier to this data, results demonstrate the population of English-tweeting Twitter users in 2015 who posted abusive misogynistic content were more likely to be classified as female when compared against a random Twitter sample from the same timeframe. For harassing and hateful speech, however, we find authors are more likely to be classified as male when compared against a random Twitter sample. Though this work does not cover threats of sexual violence, these results are consistent with prior research into misogynistic and hateful insults both on- and offline and have consequences for interventions designed to improve the quality of content in online spaces.

2. This Paper is About Lexical Propagation on Twitter. H*ckin Smart. 12/10. Would Accept!

   Jennifer Golbeck, and Cody Buntain

   In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Aug 2018

   Abs

   This paper presents an observational study of lexical propagation across online social networking platforms. By focusing on the highly followed @dog_rates Twitter account, we explore how a popular account’s unique style of language propagates outside of the account’s immediate follower community within Twitter. Initial results show a strong relationship between the prevalence of this account’s language-specific features and the account’s followership and popularity. Expanding this research across platforms, we demonstrate consistency in these results outside Twitter, as the @dog_rates vernacular shows a similarly strong relationship between use on Reddit and the account’s followership over time.

3. #pray4victims : Consistencies In Response To Disaster on Social Media

   Cody L Buntain, and Jung Kyu (Rhys) Lim

   In Proceedings of the 21st ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW), Aug 2018
4. Fake news vs satire: A dataset and analysis

   J. Golbeck, M. Mauriello, B. Auxier, and 28 more authors

   WebSci 2018 - Proceedings of the 10th ACM Conference on Web Science, Aug 2018

   Abs

   \textcopyright 2018 Association for Computing Machinery. Fake news has become a major societal issue and a technical challenge for social media companies to identify. This content is difficult to identify because the term "fake news" covers intentionally false, deceptive stories as well as factual errors, satire, and sometimes, stories that a person just does not like. Addressing the problem requires clear definitions and examples. In this work, we present a dataset of fake news and satire stories that are hand coded, verified, and, in the case of fake news, include rebutting stories. We also include a thematic content analysis of the articles, identifying major themes that include hyperbolic support or condemnation of a figure, conspiracy theories, racist themes, and discrediting of reliable sources. In addition to releasing this dataset for research use, we analyze it and show results based on language that are promising for classification purposes. Overall, our contribution of a dataset and initial analysis are designed to support future work by fake news researchers.

5. Sampling Social Media: Supporting Information Retrieval from Microblog Data Resellers with Text, Network, and Spatial Analysis

   Cody Buntain, Erin McGrath, and Brandon Behlendorf

   In Proceedings of the 51st Hawaii International Conference on System Sciences, Aug 2018

   Abs

   This paper presents a computationally assisted method for scaling researcher expertise to large, online social media datasets in which access is constrained and costly. Developed collaboratively between social and computer science researchers, this method is designed to be flexible, scalable, cost-effective, and to reduce bias in data collection. Online response to six case studies covering elections and election-related violence in Sub-Saharan African countries are explored using Twitter, a popular online microblogging platform. Results show: 1) automated query expansion can researcher mitigate bias, 2) machine learning models combining textual, social, temporal, and geographic features in social media data perform well in filtering data unrelated to the target event, and 3) these results are achievable while minimizing fee-based queries by bootstrapping with readily-available Twitter samples.

6. Identifying Stance in Controversial Topics Through Textual , Social , and Emotional Dimensions

   Abigail Bickford, Cody Buntain, Jennifer Golbeck, and 11 more authors

   Beyond Online Data, Aug 2018

2017

1. I Want to Believe: Journalists and Crowdsourced Accuracy Assessments in Twitter

   Cody Buntain, and Jennifer Golbeck

   Aug 2017

   Abs

   Evaluating information accuracy in social media is an increasingly important and well-studied area, but limited research has compared journalist-sourced accuracy assessments to their crowdsourced counterparts. This paper demonstrates the differences between these two populations by comparing the features used to predict accuracy assessments in two Twitter data sets: CREDBANK and PHEME. While our findings are consistent with existing results on feature importance, we develop models that outperform past research. We also show limited overlap exists between the features used by journalists and crowdsourced assessors, and the resulting models poorly predict each other but produce statistically correlated results. This correlation suggests crowdsourced workers are assessing a different aspect of these stories than their journalist counterparts, but these two aspects are linked in a significant way. These differences may be explained by contrasting factual with perceived accuracy as assessed by expert journalists and non-experts respectively. Following this outcome, we also show preliminary results that models trained from crowdsourced workers outperform journalist-trained models in identifying highly shared "fake news" stories.

2. A Large Labeled Corpus for Online Harassment Research

   Jennifer Golbeck, Rajesh Kumar Gnanasekaran, Raja Rajan Gunasekaran, and 25 more authors

   Proceedings of the 2017 ACM on Web Science Conference - WebSci ’17, Aug 2017
3. Automatically Identifying Fake News in Popular Twitter Threads

   Cody Buntain, and Jennifer Golbeck

   In 2017 IEEE International Conference on Smart Cloud (SmartCloud), Nov 2017
4. Capturing Micro-Expressions of Grievance to Explain Electoral Violence in Sub-Saharan Africa Fall 2017

   Erin C McGrath, Eric Dunford, Cody Buntain, and 1 more author

   In American Political Science Association, Nov 2017
5. A Large Labeled Corpus for Online Harassment Research

   Jennifer Golbeck, Zahra Ashktorab, Rashad O Banjo, and 25 more authors

   In Proceedings of the 2017 ACM on Web Science Conference, Nov 2017
6. A Large Labeled Corpus for Online Harassment Research

   Jennifer Golbeck, Rajesh Kumar Gnanasekaran, Raja Rajan Gunasekaran, and 25 more authors

   Proceedings of the 2017 ACM on Web Science Conference - WebSci ’17, Nov 2017

2016

1. Evaluating Public Response to the Boston Marathon Bombing and Other Acts of Terrorism through Twitter

   Cody Buntain, Jennifer Golbeck, Brooke Liu, and 1 more author

   In International AAAI Conference on Web and Social Media, Nov 2016

   Abs

   Twitter and other social platforms have become important communication channels during crises. While research into crisis informatics and social media is growing, the rarity of terrorist attacks in developed, Western countries complicates analysis of these specific events. To address this gap, we explore social media response to three terror events: the 2013 Boston Marathon Bombing, the 2014 Sydney Hostage Crisis, and the 2015 Charlie Hebdo Shooting. We show that, while these events do not significantly impact general Twitter usage, those users who are discussing the event behave in predictable ways across all three events. Such behaviors include increased references to the event and use of retweets, hashtags, and URLs. Furthermore, local news affiliates and law enforcement agencies (if present on social media) emerge as central actors in the networks.

2. Comparing Social Media and Traditional Surveys Around the Boston Marathon Bombing

   Cody Buntain, Erin McGrath, Jennifer Golbeck, and 2 more authors

   #Microposts: 6th Workshop on Making Sense of Microposts, Nov 2016
3. Discovering Key Moments in Social Media Streams

   Cody Buntain, Jimmy Lin, and Jennifer Golbeck

   In Consumer Communications and Networking Conference (CCNC), 2016 13th Annual IEEE, Jan 2016
4. Tracking Emigration from Conflict Areas using Social Media

   Cody Buntain, and Erin Mcgrath

   In Workshop on Social Media Demographic Research at ICWSM, Jan 2016
5. Burst Detection in Social Media Streams for Tracking Interest Profiles in Real Time

   Cody Buntain, and Jimmy Lin

   In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR ’16, Jan 2016

   Abs

   This work described RTTBurst, an end-to-end system for ingest-ing a user’s interest profiles that describe some topic of interest and identifying new tweets that might be of interest to that user using a simple model for bursts in token usage. We laid out RTTBurst’s architecture, our participation in and performance at the TREC 2015 Microblog Track, and a post hoc analysis for increasing RT-TBurst’s performance. While not as relatively performant in the Microblog Track’s real-time notification task, RTTBurst did per-form well (ranking 4th overall and second in the automatic category of Scenario B) in providing daily summaries for various interest profiles. Following the official TREC evaluation period, we were also able to increase RTTBurst’s performance but not by enough to significantly increase its overall ranking.

6. Discovering Credible Events In Near Real Time From Social Media Streams

   Cody Buntain

   University of Maryland, Jan 2016

2015

1. Powers and Problems of Integrating Social Media Data with Public Health and Safety

   Cody Buntain, Jennifer Golbeck, and Gary LaFree

   In Bloomberg Data for Good Exchange, Jan 2015
2. Trust transfer between contexts

   Cody Buntain, and Jennifer Golbeck

   Journal of Trust Management, Jan 2015

   Abs

   This paper explores the question of whether trust, de- veloped in one context, transfers into another, unrelated context. Specifically, we want to see the effects of ar- tificially stimulated prior trust as it transfers across dis- parate contexts and whether this prior trust can compen- sate for objective, qualitative information. To gather this data, we leveraged Berg’s investment game, a custom- built four-player extension of the well-known Battle- ship game, and conducted a series of experiments us- ing Amazon’s Mechanical Turk to determine how hu- mans use prior trust to guide decisions and integrate (or ignore) additional information. Our results demonstrate this trust transfer effect does exist across disparate con- texts and greatly influences human behavior regardless of provided qualitative data and is relatively robust to very poor ratings. That is, humans show a strong ten- dency to select teammates based on almost solely on prior experience with the possible players and ignoring qualitative information regarding player proficiency in the game about to be played.

3. This is Your Twitter on Drugs: Any Questions?

   Cody Buntain, and Jennifer Golbeck

   In Workshop on Modeling social media: mining big data in social media and the web (MSM 2015). Proceedings of the 24th International Conference on World Wide Web Companion Series, Jan 2015

2014

1. Trust Transfer between Contexts

   Cody Buntain, and Jennifer Golbeck

   AAAI 2014 Workshop on Incentives and Trust in E-Communities (WIT-EC’14), Jan 2014
2. Language-Agnostic Event Detection Across Sports from Twitter Using Temporal Features

   Cody Buntain

   In Workshop on Large-Scale Sports Analytics, Jan 2014
3. Comparing Algorithms for Detectting Abrupt Change Points in Data

   Cody Buntain, Christopher Natoli, and Miroslav Živković

   In Super Computing 2014 Poster Presentations, Nov 2014
4. Identifying social roles in reddit using network structure

   Cody Buntain, and Jennifer Golbeck

   In Workshop on Modeling social media: mining big data in social media and the web (MSM 2014), in 23rd International World Wide Web Conference, WWW ’14, Nov 2014
5. Leveraging Fee-Based , Imperfect Advisors in Human-Agent Games of Trust

   Cody Buntain, Amos Azaria, and Sarit Kraus

   AAAI, Nov 2014

2013

1. Computational trust assessment of open media data

   Nadya Belov, Jason Schlachter, Cody Buntain, and 1 more author

   In Multimedia and Expo Workshops (ICMEW), 2013 IEEE International Conference on, Nov 2013

2012

1. Advice and trust in games of choice

   Cody Buntain, Jennifer Golbeck, Dana Nau, and 1 more author

   In Privacy, Security and Trust (PST), 2012 Tenth Annual International Conference on, Nov 2012

2009

1. Spyglass: A system for ontology based document retrieval and visualization

   John Rushing, Todd Berendes, Hong Lin, and 2 more authors

   The 22nd International FLAIRS Conference, Nov 2009

2008

1. 3D ontology visualization in semantic search

   Cody Buntain

   In Proceedings of the 46th Annual Southeast Regional Conference on XX, Nov 2008