TY - UNPB T1 - The Impacts of Automation on Journalists Y1 - 2021 A1 - Ayse Dalgali A1 - Kevin Crowston AB - We examined journalists’ tasks as described on the Occupational Information Network (O*NET) website. The tasks we analyzed specifically are those assigned to reporters and correspondents (27-3022.00 - Reporters and Correspondents). From the tasks, we developed a workflow diagram for journalist’s work. Reviewing the previous relevant literature, we made predictions to explore which journalism tasks are more automatable and which are less automatable, and how the trending advent of technologies such automatic content generation may change journalists’ roles. Highlighting research and practical implications, we conclude the paper with limitations and areas for further research. ER - TY - CONF T1 - Algorithmic Journalism and Its Impacts on Work T2 - Computation + Journalism Symposium Y1 - 2020 A1 - Ayse Dalgali A1 - Kevin Crowston AB -

In the artificial intelligence era, algorithmic journalists can produce news reports in natural language from structured data thanks to natural language generation (NLG) algorithms. This paper presents several algorithmic content generation models and discusses the impacts of algorithmic journalism on work within a framework consisting of three levels: replacing tasks of journalists, increasing efficiency, and developing new capabilities within journalism. The findings indicate that algorithmic journalism technology may lead some changes in journalism by enabling individual users to produce their own stories. This paper may contribute to an understanding of how algorithmic news is created and how algorithmic journalism technology impacts work.

JF - Computation + Journalism Symposium UR - https://cpb-us-w2.wpmucdn.com/express.northeastern.edu/dist/d/53/files/2020/02/CJ_2020_paper_26.pdf ER - TY - Generic T1 - Factors Influencing Approval of Wikipedia Bots T2 - Hawai'i International Conference on System Science Y1 - 2020 A1 - Ayse Dalgali A1 - Kevin Crowston AB -

Before a Wikipedia bot is allowed to edit, the operator of the bot must get approval. The Bot Approvals Group (BAG), a committee of Wikipedia bot developers, users and editors, discusses each bot request to reach consensus regarding approval or denial. We examine factors related to approval of a bot by analyzing 100 bots’ project pages. The results suggest that usefulness, value-based decision making and the bot’s status (e.g., automatic or manual) are related to approval. This study may contribute to understanding decision making regarding the human-automation boundary and may lead to developing more efficient bots.

JF - Hawai'i International Conference on System Science CY - Wailea, HI ER - TY - Generic T1 - Sharing open deep learning models T2 - Proceedings of the 52nd Hawai'i International Conference on System Sciences (HICSS-52) Y1 - 2019 A1 - Ayse Dalgali A1 - Kevin Crowston AB -

We examine how and why trained deep learning (DL) models are shared, and by whom, and why some developers share their models while others do not. Prior research has examined sharing of data and software code, but DL models are a hybrid of the two. The results from a Qualtrics survey administered to GitHub users and academics who publish on DL show that a diverse population shares DL models, from students to computer/data scientists. We find that motivations for sharing include: increasing citation rates; contributing to the collaboration of developing new DL models; encouraging to reuse; establishing a good reputation; receiving feedback to improve the model; and personal enjoyment. Reasons for not sharing include: lack of time; thinking that their models would not be interesting for others; and not having permission for sharing. The study contributes to our understanding of motivations for participating in a novel form of peer-production.

JF - Proceedings of the 52nd Hawai'i International Conference on System Sciences (HICSS-52) UR - http://hdl.handle.net/10125/59650 ER -