@unpublished {9999, title = {The Impacts of Automation on Journalists}, year = {2021}, abstract = {We examined journalists{\textquoteright} tasks as described on the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{\textquoteright}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{\textquoteright} roles. Highlighting research and practical implications, we conclude the paper with limitations and areas for further research. }, author = {Ayse Dalgali and Kevin Crowston} } @conference {2020, title = {Algorithmic Journalism and Its Impacts on Work}, booktitle = {Computation + Journalism Symposium }, year = {2020}, edition = {(cancelled due to COVID; presented in 2021)}, abstract = {

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.

}, url = {https://cpb-us-w2.wpmucdn.com/express.northeastern.edu/dist/d/53/files/2020/02/CJ_2020_paper_26.pdf}, attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/CJ_2020_paper_26.pdf}, author = {Ayse Dalgali and Kevin Crowston} } @proceedings {2020, title = {Factors Influencing Approval of Wikipedia Bots}, year = {2020}, address = {Wailea, HI}, abstract = {

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{\textquoteright} project pages. The results suggest that usefulness, value-based decision making and the bot{\textquoteright}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.

}, doi = {10.24251/HICSS.2020.018}, attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/HICSS_WikipediaPaper_3.9.new\%20kc\%20\%282\%29.pdf}, author = {Ayse Dalgali and Kevin Crowston} } @proceedings {9999, title = {Sharing open deep learning models}, year = {2019}, abstract = {

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.

}, doi = {10.24251/HICSS.2019.256}, url = { http://hdl.handle.net/10125/59650}, attachments = {https://crowston.syr.edu/sites/crowston.syr.edu/files/Hicss\%20paper2018_DalgaliCrowston.pdf}, author = {Ayse Dalgali and Kevin Crowston} }