Publications Database

Welcome to the new Schulich Peer-Reviewed Publication Database!

The database is currently in beta-testing and will be updated with more features as time goes on. In the meantime, stakeholders are free to explore our faculty’s numerous works. The left-hand panel affords the ability to search by the following:

  • Faculty Member’s Name;
  • Area of Expertise;
  • Whether the Publication is Open-Access (free for public download);
  • Journal Name; and
  • Date Range.

At present, the database covers publications from 2012 to 2020, but will extend further back in the future. In addition to listing publications, the database includes two types of impact metrics: Altmetrics and Plum. The database will be updated annually with most recent publications from our faculty.

If you have any questions or input, please don’t hesitate to get in touch.

 

Search Results

Tahmina Ahmed and Gregory D. Saxton (2026). "The Effects of Bots on Market Reactions to Earnings News", Journal of Information Systems, 1–23.

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Keywords
Abstract Social media platforms such as Twitter influence capital markets by rapidly disseminating information; yet, this environment is increasingly shaped by nonhuman bots. Building on theories of investor attention and information salience, we examine whether bots amplify market reactions to earnings news by directing attention toward larger surprises. Using machine learning to classify 12.02 million tweets discussing S&P 1,500 firms in 2018, we measure firm-specific abnormal bot activity and analyze its association with market responses to earnings announcements. We find that bot activity amplifies the relationship between earnings surprises and abnormal returns. Additional analyses reveal that this effect is stronger when bot sentiment is positive but diminishes with excessive positivity, varies by bot type, and is most pronounced for firms with fewer analysts, further supporting our investor attention argument. Our findings highlight bots’ roles as “attention amplifiers” and underscore the need for greater scrutiny of algorithmic actors in financial markets.

Dean Neu, Gregory D. Saxton (2023). "Twitter Bots, Democratic Deliberation and Social Accountability: The Case of #OccupyWallStreet", Accounting, Auditing & Accountability Journal.

Open Access Download

Keywords
Abstract

Purpose

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

Design/methodology/approach

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

Findings

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

Research limitations/implications

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

Originality/value

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.