Arda Kaz’s scientific contributions

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Publications (1)

Figure 2: Comparison of Kendall's τ rank correlation (on newsworthiness judgements) and SBERT cosine similarity (on articles) across news outlets.
Figure 4: Illustration of our deterministic bootstrapping algorithm and a failure case. Here, when non-article links exist, we misunderstand the full area of an article, excluding the text below.
Figure 5: Different analyses we run on bounding boxes across time: average locations of bounding boxes on a homepage, locations where articles are added first, locations where they are removed, and the average time articles in various locations spend.
Figure 6: With our suite of tools for parsing homepages, we can examine on a granular level the movement of an article across the homepage.
Figure 8: When sorting our sources to determine the ones most difficult for the DOM-Tree algorithm, we define the Average Similarity score to be a general measure as to how well the bounding box's text match the article's JSON file containing text/link pairs. High similarity score means high bounding box accuracy, and vice versa.

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NewsHomepages: Homepage Layouts Capture Information Prioritization Decisions
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November 2024

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2 Reads

Ben Welsh

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Naitian Zhou

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Arda Kaz

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Information prioritization plays an important role in how humans perceive and understand the world. Homepage layouts serve as a tangible proxy for this prioritization. In this work, we present NewsHomepages, a large dataset of over 3,000 new website homepages (including local, national and topic-specific outlets) captured twice daily over a three-year period. We develop models to perform pairwise comparisons between news items to infer their relative significance. To illustrate that modeling organizational hierarchies has broader implications, we applied our models to rank-order a collection of local city council policies passed over a ten-year period in San Francisco, assessing their "newsworthiness". Our findings lay the groundwork for leveraging implicit organizational cues to deepen our understanding of information prioritization.

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