AngleKindling: Supporting Journalistic Angle Ideation with Large Language Models

Petridis, Savvas; Diakopoulos, Nicholas; Crowston, Kevin; Hansen, Mark; Henderson, Keren; Jastrzebski, Stan; Nickerson, Jeffrey V.; Chilton, Lydia B.

doi:10.1145/3544548.3580907

Cited by 45 publications

(16 citation statements)

References 60 publications

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“…This shift redefines AI as an active collaborator, expanding its use in diverse contexts like story writing [7,18,40,62,63,74] and creative tasks [19,25,28,41,72]. These adaptable models support specialized tasks such as journalistic ideation [55], visual storyboarding [18], screenplay co-creation [46], emotional composition [52], crafting help requests [36], and generating scientific paper ideas [30].…”

Section: Related Work 21 Ai-based Writing Tools and Human-ai Collabor...mentioning

confidence: 99%

“…In the context of AI-supported writing, the scaffolding dynamics are highly complex. Expert writers may eschew AI assistance, fearing it could stifle their creativity or impose a "cookie-cutter" structure on their unique style [55]. Conversely, non-experts or those experiencing writer's block might find initial AI suggestions invaluable to kick-start their writing process, editing and refining AI-generated content to better align it with their own voice and objectives [32].…”

Section: Introductionmentioning

confidence: 99%

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Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models

Dhillon,

Molaei,

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Advances in language modeling have paved the way for novel human-AI co-writing experiences. This paper explores how varying levels of scaffolding from large language models (LLMs) shape the co-writing process. Employing a within-subjects field experiment with a Latin square design, we asked participants (N=131) to respond to argumentative writing prompts under three randomly sequenced conditions: no AI assistance (control), next-sentence suggestions (low scaffolding), and next-paragraph suggestions (high scaffolding). Our findings reveal a U-shaped impact of scaffolding on writing quality and productivity (words/time). While low scaffolding did not significantly improve writing quality or productivity, high scaffolding led to significant improvements, especially benefiting non-regular writers and less tech-savvy users. No significant cognitive burden was observed while using the scaffolded writing tools, but a moderate decrease in text ownership and satisfaction was noted. Our results have broad implications for the design of AI-powered writing tools, including the need for personalized scaffolding mechanisms.

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Section: Related Work 21 Ai-based Writing Tools and Human-ai Collabor...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models

Dhillon,

Molaei,

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…Moreover, it is essential to design effective mechanisms for users to verify content efficiently. Inspired by AngleKindling's system design [45] that shows the connection between LLM-generated angles and source material to support journalists, one approach involves making specific sections of the paper, particularly relevant to the LLM-generated descriptions, accessible-such as through highlighting or indicating whether there is clear citations with a hyperlink to source papers. We also can indicate whether there is clear source material that the output is grounded on by marking citations at the end of each sentence.…”

Section: Accuracy Of Llm-generated Descriptionsmentioning

confidence: 99%

PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers

Lee,

Kang,

Latzke

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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With the rapid growth of scholarly archives, researchers subscribe to "paper alert" systems that periodically provide them with recommendations of recently published papers that are similar to previously collected papers. However, researchers sometimes struggle to make sense of nuanced connections between recommended papers and their own research context, as existing systems only present paper titles and abstracts. To help researchers spot these connections, we present PaperWeaver, an enriched paper alerts system that provides contextualized text descriptions of recommended papers based on user-collected papers. PaperWeaver employs a computational method based on Large Language Models (LLMs) to infer users' research interests from their collected papers, extract context-specific aspects of papers, and compare recommended and collected papers on these aspects. Our user study (N=15) showed that participants using PaperWeaver were able to better understand the relevance of recommended papers and triage them more confidently when compared to a baseline that presented the related work sections from recommended papers. CCS CONCEPTS• Human-centered computing → Interactive systems and tools; Empirical studies in HCI; Natural language interfaces.

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“…Prompt-based LLMs are increasingly used in the CHI community [20,56,76,110], primarily contributing to the development of applications like chatbots [46] and tools for co-writing [56], virtual simulations [108], story-telling [23], and visualization enhancement [96]. Such studies have primarily focused on using LLMs as production tools [56] rather than tools for analysis.…”

Section: Harnessing Large Language Models In Computational Social Sci...mentioning

confidence: 99%

Leveraging Prompt-Based Large Language Models: Predicting Pandemic Health Decisions and Outcomes Through Social Media Language

Ding,

Carik,

Gunturi

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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We introduce a multi-step reasoning framework using promptbased LLMs to examine the relationship between social media language patterns and trends in national health outcomes. Grounded in fuzzy-trace theory, which emphasizes the importance of "gists" of causal coherence in effective health communication, we introduce Role-Based Incremental Coaching (RBIC), a prompt-based LLM framework, to identify gists at-scale. Using RBIC, we systematically extract gists from subreddit discussions opposing COVID-19 health measures (Study 1). We then track how these gists evolve across Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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AngleKindling: Supporting Journalistic Angle Ideation with Large Language Models

Cited by 45 publications

References 60 publications

Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models

Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models

PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers

Leveraging Prompt-Based Large Language Models: Predicting Pandemic Health Decisions and Outcomes Through Social Media Language

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