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REVIEW 2 major objections 5 minor 79 references

A Research Vision for Web Search on Emerging Topics

T0 review · 2 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Web search on emerging topics needs a dedicated research agenda, organized around three research questions.

desk verdict A competent, honest vision paper that structures a worthwhile research agenda around emerging topics in web search, with the missing operational definition explicitly acknowledged as the first step; deserves a place at a workshop but not more. read the letter →

arxiv 2509.10212 v1 pith:3UUOGYIB submitted 2025-09-12 cs.IR

Alisa Rieger , Stefan Dietze , Ran Yu This is my paper · ORCID
classification cs.IR
keywords websearchemergingtopicsinformationbehavioropinionformationknowledgegainalgorithmicauditingmisinformationinterfacedesign
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that web search is a poor fit for emerging topics—fast-moving issues such as breaking news or new policy debates—because the information is scarce, changing quickly, and open to manipulation, and because standard ranking signals like clicks and link structure do not exist yet. It proposes a research vision in which search systems would be deliberately designed to support three outcomes: effective knowledge gain, awareness that the topic is still developing, and responsible opinion formation. The agenda is organized around three research questions: understand how current engines handle emerging topics, determine what users and society need from such systems, and build systems that meet those needs. The paper itself flags the load-bearing gap, noting in Section 3.1 that there is no concrete operational definition of "emerging topics" and that developing one is a critical first step for everything that follows.

What carries the argument

The load-bearing structure is the three-question agenda—audit the status quo (RQ1), determine requirements (RQ2), build supportive systems (RQ3)—anchored in a guiding vision the paper calls a "sociotechnical imaginary": a picture of a healthy, emancipatory information ecosystem that prioritizes informed citizens, agency, and transparency. The operational pivot is the definition of "emerging topics" itself: the paper treats it as a researchable class defined by temporal dynamics and data scarcity, and it assembles existing methods—algorithmic auditing, search-as-learning user studies, participatory design, topic and event detection, data-sparse ranking, misinformation detection, and summarization—as starting points that would have to be adapted to those dynamics.

What would settle it

Take a set of topics that intuitively feel emerging—a sudden policy announcement, a breaking disaster, a newly forming meme—and ask raters to apply candidate operational definitions based on query-volume surges, news-coverage growth, and link-graph sparsity; if the definitions disagree substantially on which topics count, or cannot be applied consistently across judges, the central premise fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that emerging topics form a distinct class of search tasks with distinct risks, not just a harder version of ordinary search. Because the body of knowledge is still forming, the available information is sparse, inconsistent, and often untrustworthy, while the behavioral and structural signals that search engines normally rely on are missing; the result is that users can be left with incomplete or misleading impressions, sometimes with false certainty. The paper therefore calls for a coordinated research program that audits current search engines on emerging topics, specifies requirements through user research, expert consultation, and participatory design, and develops detection, ranking, and summarization methods that work in data-sparse, rapidly changing conditions—all in service of a "sociotechnical imaginary" of a healthy and emancipatory information ecosystem.

Load-bearing premise

The agenda collapses if "emerging topic" cannot be defined concretely enough for researchers to agree on what counts, measure the current state of search, and evaluate new systems against it.

Editorial extensions

If this is right

  • Search engines on emerging topics would be evaluated by whether users actually gain correct knowledge and remain aware of uncertainty, not just by relevance and engagement.
  • An operational definition of emerging topics would make it possible to run systematic audits of search engines, much like existing audits of election-related and gender-biased results.
  • Ranking and retrieval methods that work without link graphs and clickthrough data would change how search behaves in the early hours and days of a breaking topic.
  • Summaries for emerging topics would need to display multiple viewpoints and signal fast-changing or unresolved facts, instead of presenting one confident summary.
  • Interface features could be designed to encourage more diligent search behavior, building on existing evidence that snippets and rankings shape users' attitudes and opinions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the definitional step succeeds, "emerging" could become a measurable dimension—novelty, volatility, controversy, data sparsity—rather than a yes/no label, allowing audits and studies to compare topics across that spectrum.
  • The design principles this agenda motivates, such as uncertainty indicators and viewpoint diversity, could plausibly improve web search on stable-but-debated topics as well, giving the vision broader reach than the paper explicitly claims.
  • A natural early test would be an uncertainty-aware summary interface compared against a standard LLM summary on a real breaking topic, measuring whether users' confidence tracks the topic's actual volatility.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. The paper presents a research vision for web search on emerging topics—issues whose knowledge base is still evolving, often linked to current events. The authors argue that current search engines and LLM-based features poorly serve users seeking information on such topics, due to data sparsity, rapid change, and susceptibility to misinformation and bias. They propose three overarching research questions: RQ1 (understand the status quo of how search engines handle emerging topics and how users interact with them), RQ2 (determine system and interface requirements to support effective knowledge gain, awareness of dynamic topic nature, and responsible opinion formation), and RQ3 (develop supportive search systems, including detection, retrieval, ranking, and summarization). For each question, the paper surveys relevant literature and methodological pointers, and it concludes with anticipated research challenges such as temporal dynamics, study design trade-offs, data access limitations, and evaluation complexity. The paper is explicitly a vision/agenda-setting piece rather than an empirical study.

Significance. If the proposed agenda is pursued, it would broaden information retrieval research from optimizing relevance toward supporting careful engagement with evolving and contested topics, complementing existing work on debated topics, algorithmic auditing, and misinformation. The paper is useful as a concise synthesis of prior work across algorithmic auditing, interactive IR, HCI, and NLP, and it connects these strands to a normative sociotechnical imaginary. Its strength is that it does not overclaim: it explicitly identifies the lack of an operational definition of emerging topics as a critical first step, and it candidly discusses methodological challenges, including rapid deployment and ecological validity. The paper makes no empirical claims, so the absence of an operational definition is a limitation rather than a falsified assertion, but it remains a load-bearing issue for the coherence of the proposed research program.

major comments (2)
  1. [Section 3.1, first bullet] The paper acknowledges lacking a concrete operational definition of 'emerging topics' and calls its development 'a critical first step for all subsequent research.' However, it does not address the risk that the construct is multidimensional (e.g., novelty, popularity, controversy, rate of change) and that plausible operationalizations—query-volume bursts, news-story lifecycle stage, entity freshness, social-media spike detection—may select largely disjoint topic sets. If different operationalizations do not overlap, findings from RQ1 would not transfer across definitions, and RQ3 systems tuned to one definition could fail on another. The paper should at least outline criteria for evaluating candidate definitions (e.g., coverage, stability, inter-rater agreement) or propose a provisional working definition to anchor the agenda.
  2. [Section 3.3, first guiding question] The question 'How can we detect emerging topics?' introduces a potential circularity with RQ1. If detection methods effectively supply the operational definition of 'emerging topic,' then RQ1's audit of the status quo becomes contingent on the detector's assumptions, and the claimed separation between understanding the status quo (RQ1) and building systems (RQ3) is blurred. The paper should clarify how the operational definition called for in RQ1 relates to the detection methods surveyed in RQ3, or explicitly acknowledge that detection is part of the definitional problem rather than a downstream engineering task.
minor comments (5)
  1. [Section 4, paragraph 2] There is a duplicated word: 'tighter API restrictions restrictions hinder access' should read 'tighter API restrictions hinder access.'
  2. [Figure 2] The figure presenting the three research questions is never referenced in the body text after Section 2; consider adding an explicit pointer and a slightly more descriptive caption so that the figure is self-contained.
  3. [Keywords/Abstract] The keyword 'Emancipatory Information Ecosystem' is used without being defined; Section 2 introduces the sociotechnical imaginary, but a brief definition of 'emancipatory' in this context would help readers who encounter the keyword before reading the full text.
  4. [Reference [62]] Reference [62] lists the publication year as '????'; the bibliographic entry should be completed or the reference should be replaced with a version with a full publication date.
  5. [Section 3.3, summarization paragraph] The sentence 'we thus propose going beyond common performance metrics, by conducting user studies that measure their effect on search behavior and search outcomes, such as knowledge gain' could be sharpened by specifying which concrete metrics are already available from the literature cited in Section 3.1, so that the call is actionable rather than purely programmatic.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a research agenda with no fitted predictions or derived claims.

full rationale

This is a position/vision paper that proposes research questions and surveys relevant literature; it makes no quantitative derivations, fits no parameters, and offers no empirical predictions that could reduce to its inputs. The central commitment—that search systems and interfaces should support knowledge acquisition, awareness of topic dynamics, and responsible opinion formation—is explicitly normative and anchored in a stated sociotechnical imaginary rather than derived from data or from the authors' prior work. The paper's many self-citations (e.g., refs. [6], [14], [28], [30], [38] and the authors' knowledge-gain work among refs. [31]–[34]) are used as background pointers to prior user studies and methods, not as load-bearing justifications for any claim whose truth depends on those citations. The one potentially delicate point is the paper's own admission in Section 3.1 that 'we lack a concrete operational definition' of emerging topics and that developing one 'is a critical first step for all subsequent research.' This is an acknowledged open problem, not a hidden circularity: the paper does not presuppose a specific operationalization, and the proposed RQ1 explicitly includes the definitional question as an object of study. Similarly, Section 3.3's question 'How can we detect emerging topics?' is presented as a future challenge, and the paper does not use a detector to define the target class, so there is no present construction in which detection supplies the definition. The skeptic's concern that different operationalizations might disagree is a legitimate correctness or feasibility risk for the agenda, but it is not a circularity in the paper's reasoning. No equation or fitted parameter is renamed as a prediction, no uniqueness theorem from the authors is invoked to force a choice, and no known result is repackaged under new coordinates. The derivation chain is therefore self-contained in the only sense applicable to a research vision: its claims are openly stated goals and literature-informed suggestions rather than conclusions forced by their own premises.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper introduces no free parameters or invented entities. Its central claim is supported by domain assumptions about the centrality of web search, the definability of emerging topics, the availability of data, and the normative goals of an emancipatory information ecosystem. These assumptions are stated in Sections 1 and 2 and are reasonable but untested.

assumptions (5)
  • domain assumption Web search is a primary gateway to addressing a wide range of information needs, including on emerging topics.
    Stated in Section 1; the entire vision assumes search engines are central to how people learn about emerging topics.
  • domain assumption Emerging topics form a coherent and researchable category, despite lacking an operational definition.
    Section 3.1 admits 'we lack a concrete operational definition' but treats the category as meaningful and definable; this underpins all three research questions.
  • domain assumption A healthy and emancipatory information ecosystem is an appropriate normative goal for search system design.
    Section 2 anchors the research vision in this sociotechnical imaginary, citing Mitra [3] and Shah and Bender [8]; this is a value choice, not an empirical fact.
  • domain assumption For emerging topics, large-scale behavioral signals and structural data are unavailable, which is a stable challenge.
    Section 1; this motivates the need for new ranking and quality-assessment methods, and the paper acknowledges it as a persistent constraint.
  • domain assumption Information on emerging topics is prone to manipulation, misinformation, and bias.
    Section 1 and references [6,7]; the problem framing depends on this being a real and substantial risk.

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Cite this review

Pith. "Pith review of A Research Vision for Web Search on Emerging Topics." pith.science (2026). https://pith.science/paper/3UUOGYIB

@misc{pith2026250910212,
  author       = {Pith},
  title        = {Pith review of: A Research Vision for Web Search on Emerging Topics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3UUOGYIB}},
  note         = {Machine review of arXiv:2509.10212}
}
read the original abstract

We regularly encounter information on novel, emerging topics for which the body of knowledge is still evolving, which can be linked, for instance, to current events. A primary way to learn more about such topics is through web search. However, information on emerging topics is sparse and evolves dynamically as knowledge grows, making it uncertain and variable in quality and trustworthiness and prone to deliberate or accidental manipulation, misinformation, and bias. In this paper, we outline a research vision towards search systems and interfaces that support effective knowledge acquisition, awareness of the dynamic nature of topics, and responsible opinion formation among people searching the web for information on emerging topics. To realize this vision, we propose three overarching research questions, aimed at understanding the status quo, determining requirements of systems aligned with our vision, and building these systems. For each of the three questions, we highlight relevant literature, including pointers on how they could be addressed. Lastly, we discuss the challenges that will potentially arise in pursuing the proposed vision.

Figures

Figures reproduced from arXiv: 2509.10212 by the authors.

Figure 1
Figure 1. Search Page Example from Google after issuing the query trump’s tariffs (left) and after clicking see results about feature (right), retrieved on 17 April 2025 news articles from reputable outlets and offer an option to refine the search. Once refined, the top results prioritize Wikipedia entries accompanied by a snippet of the article and followed by related news coverage. These interface features seem carefully de… view at source ↗
Figure 2
Figure 2. Overview of Main Research Questions 3.1. Understanding the Status Quo (RQ1) To gain insights into the status quo of web search on emerging topics, we can consider the following guiding questions, among others: • What is a meaningful and operational definition of emerging topics in the context of information retrieval research? • How do different search engines handle and present information on emerging topics? • How… view at source ↗

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Works this paper leans on

79 extracted references · 50 canonical work pages

  1. [1]

    Taylor, M

    S. Taylor, M. Boniface, B. Pickering, P. Grace, V. Bakir, d. boyd, S. Engesser, R. Epstein, N. Fawzi, P. Fernbach, D. Fisher, B. G. Gardner, K. Jacobs, S. Jacobson, B. Krämer, A. Kucharski, A. McStay, H. Mercier, M. Metzger, F. Polletta, W. Quattrociocchi, S. Sloman, D. Sperber, C. H. B. M. Spierings, C. Wardle, F. Zollo, A. Zubiaga, Opinion forming in th...

    work page doi:10.5281/zenodo.1468575 2018

  2. [2]

    Trattner, D

    C. Trattner, D. Jannach, E. Motta, I. Costera Meijer, N. Diakopoulos, M. Elahi, A. L. Opdahl, B. Tessem, N. Borch, M. Fjeld, L. Øvrelid, K. De Smedt, H. Moe, Responsible media technology and AI: Challenges and research directions, AI and Ethics 2 (2022) 585–594. doi: 10.1007/ s43681-021-00126-4

    work page 2022

  3. [3]

    Mitra, Search and Society: Reimagining Information Access for Radical Futures, Information Retrieval Research 1 (2025) 47–92

    B. Mitra, Search and Society: Reimagining Information Access for Radical Futures, Information Retrieval Research 1 (2025) 47–92. doi:10.54195/irrj.19654

    work page doi:10.54195/irrj.19654 2025

  4. [4]

    C. Shah, E. M. Bender, Situating Search, in: Proceedings of the 2022 Conference on Human Information Interaction and Retrieval, CHIIR ’22, Association for Computing Machinery, New York, NY, USA, 2022, pp. 221–232. doi:10.1145/3498366.3505816

    arXiv 2022

  5. [5]

    Lorenz-Spreen, L

    P. Lorenz-Spreen, L. Oswald, S. Lewandowsky, R. Hertwig, A systematic review of worldwide causal and correlational evidence on digital media and democracy, Nature Human Behaviour 7 (2023) 74–101. doi:10.1038/s41562-022-01460-1

    doi:10.1038/s41562-022-01460-1 2023

  6. [6]

    Rieger, T

    A. Rieger, T. Draws, N. Mattis, D. Maxwell, D. Elsweiler, U. Gadiraju, D. McKay, A. Bozzon, M. S. Pera, Responsible Opinion Formation on Debated Topics in Web Search, Advances in Information Retrieval - 46th European Conference on Information Retrieval, ECIR 2024, Proceedings (2024) 437–465. doi:10.1007/978-3-031-56066-8_32

    work page doi:10.1007/978-3-031-56066-8_32 2024

  7. [7]

    Golebiewski, D

    M. Golebiewski, D. Boyd, Data Voids: Where Missing Data Can Easily Be Exploited, Report, Data & Society Research Institute, 2019

    work page 2019

  8. [8]

    C. Shah, E. M. Bender, Envisioning Information Access Systems: What Makes for Good Tools and a Healthy Web?, ACM Trans. Web 18 (2024) 33:1–33:24. doi:10.1145/3649468

    doi:10.1145/3649468 2024

Show all 79 references
  1. [9]

    Castillo, M

    C. Castillo, M. El-Haddad, J. Pfeffer, M. Stempeck, Characterizing the life cycle of online news stories using social media reactions, in: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, CSCW ’14, Association for Computing Mach...

    2014

  2. [10]

    Lorenz-Spreen, B

    P. Lorenz-Spreen, B. M. Mønsted, P. Hövel, S. Lehmann, Accelerating dynamics of collective attention, Nature Communications 10 (2019) 1759. doi:10.1038/s41467-019-09311-w

    2019 doi

  3. [11]

    Cataldi, L

    M. Cataldi, L. D. Caro, C. Schifanella, Personalized emerging topic detection based on a term aging model, ACM Transactions on Intelligent Systems and Technology (TIST) 5 (2014) 1–27

    2014

  4. [12]

    Zubiaga, D

    A. Zubiaga, D. Spina, R. Martínez, V. Fresno, Real-time classification of Twitter trends, Journal of the Association for Information Science and Technology 66 (2015) 462–473. doi:10.1002/asi.23186

    2015 doi

  5. [13]

    X. Hu, W. Ma, C. Chen, S. Wen, J. Zhang, Y. Xiang, G. Fei, Event detection in online social network: Methodologies, state-of-art, and evolution, Computer Science Review 46 (2022) 100500. doi:10.1016/j.cosrev.2022.100500

    2022

  6. [14]

    Rieger, Striving for responsible opinion formation in web search on debated topics, 2024

    A. Rieger, Striving for responsible opinion formation in web search on debated topics, 2024. doi:10.4233/uuid:703a1aad-d585-459a-b0b3-ac55d9e98fcd

    2024 doi

  7. [15]

    T. T. Hills, The Dark Side of Information Proliferation, Perspectives on Psychological Science 14 (2019) 323–330. doi:10.1177/1745691618803647

    2019 doi

  8. [16]

    Ulloa, M

    R. Ulloa, M. Makhortykh, A. Urman, Scaling up search engine audits: Practical insights for algorithm auditing, Journal of Information Science 50 (2024) 404–419. doi: 10.1177/ 01655515221093029

    2024

  9. [17]

    Urman, M

    A. Urman, M. Makhortykh, R. Ulloa, The Matter of Chance: Auditing Web Search Results Related to the 2020 U.S. Presidential Primary Elections Across Six Search Engines, Social Science Computer Review 40 (2022) 1323–1339. doi:10.1177/08944393211006863

    2022 doi

  10. [18]

    R. E. Robertson, S. Jiang, K. Joseph, L. Friedland, D. Lazer, C. Wilson, Auditing partisan audience bias within google search, Proceedings of the ACM on Human-Computer Interaction 2 (2018) 1–22

    2018

  11. [19]

    Metaxa, J

    D. Metaxa, J. S. Park, J. A. Landay, J. Hancock, Search Media and Elections: A Longitudinal Investigation of Political Search Results, Proc. ACM Hum.-Comput. Interact. 3 (2019) 129:1–129:17. doi:10.1145/3359231

    2019 doi

  12. [20]

    D. Hu, S. Jiang, R. E. Robertson, C. Wilson, Auditing the Partisanship of Google Search Snippets, in: The World Wide Web Conference, WWW ’19, Association for Computing Machinery, New York, NY, USA, 2019, pp. 693–704. doi:10.1145/3308558.3313654

    2019

  13. [21]

    Otterbacher, J

    J. Otterbacher, J. Bates, P. Clough, Competent Men and Warm Women: Gender Stereotypes and Backlash in Image Search Results, in: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI ’17, Association for Computing Machinery, New York, NY, USA, 2017, ...

    2017

  14. [22]

    V. K. Singh, M. Chayko, R. Inamdar, D. Floegel, Female librarians and male computer programmers? Gender bias in occupational images on digital media platforms, Journal of the Association for Information Science and Technology 71 (2020) 1281–1294. doi:10.1002/asi.24335

    2020 doi

  15. [23]

    Draws, N

    T. Draws, N. Roy, O. Inel, A. Rieger, R. Hada, M. O. Yalcin, B. Timmermans, N. Tintarev, Viewpoint Diversity in Search Results, in: J. Kamps, L. Goeuriot, F. Crestani, M. Maistro, H. Joho, B. Davis, C. Gurrin, U. Kruschwitz, A. Caputo (Eds.), Advances in Information Retrieval,...

    2023 doi

  16. [24]

    Trielli, N

    D. Trielli, N. Diakopoulos, Search as News Curator: The Role of Google in Shaping Attention to News Information, in: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI ’19, Association for Computing Machinery, New York, NY, USA, 2019, pp. 1–15. d...

    2019

  17. [26]

    M. Bink, S. Schwarz, T. Draws, D. Elsweiler, Investigating the Influence of Featured Snippets on User Attitudes, in: Proceedings of the 2023 Conference on Human Information Interaction and Retrieval, CHIIR ’23, Association for Computing Machinery, New York, NY, USA, 2023, pp. ...

    2023

  18. [27]

    Sharma, Q

    N. Sharma, Q. V. Liao, Z. Xiao, Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information Seeking, in: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, CHI ’24, Association for Computing Machinery, New York, NY, USA, 2024...

    2024

  19. [29]

    R. E. Robertson, J. Green, D. J. Ruck, K. Ognyanova, C. Wilson, D. Lazer, Users choose to engage with more partisan news than they are exposed to on Google Search, Nature 618 (2023) 342–348. doi:10.1038/s41586-023-06078-5

    2023 doi

  20. [30]

    Rieger, S

    A. Rieger, S. Kulane, U. Gadiraju, M. S. Pera, Disentangling Web Search on Debated Topics: A User-Centered Exploration, in: Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, UMAP ’24, Association for Computing Machinery, New York, NY, USA...

    2024

  21. [31]

    C. Otto, R. Yu, G. Pardi, J. von Hoyer, M. Rokicki, A. Hoppe, P. Holtz, Y. Kammerer, S. Dietze, R. Ew- erth, Predicting knowledge gain during web search based on multimedia resource consumption, in: International Conference on Artificial Intelligence in Education, Springer, 20...

    2021

  22. [32]

    R. Yu , U. Gadiraju, P. Holtz, M. Rokicki, P. Kemkes, S. Dietze, Predicting user knowledge gain in informational search sessions, in: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, 2018, pp. 75–84

    2018

  23. [33]

    Gadiraju, R

    U. Gadiraju, R. Yu, S. Dietze, P. Holtz, Analyzing knowledge gain of users in informational search sessions on the web, in: Proceedings of the 2018 Conference on Human Information Interaction & Retrieval, 2018, pp. 2–11

    2018

  24. [34]

    R. Yu , R. Tang, M. Rokicki, U. Gadiraju, S. Dietze, Topic-independent modeling of user knowledge in informational search sessions, Information Retrieval Journal (2021) 1–29

    2021

  25. [35]

    X. Peng, Q. Xu, Y. Chen, C. Zhou, Y. Ge, N. Li, An eye tracking study: positive emotional interface design facilitates learning outcomes in multimedia learning?, International Journal of Educational Technology in Higher Education 18 (2021) 1–18

    2021

  26. [36]

    R. W. White, Belief dynamics in web search, Journal of the Association for Information Science and Technology 65 (2014) 2165–2178. doi:10.1002/asi.23128

    2014 doi

  27. [37]

    Draws, N

    T. Draws, N. Tintarev, U. Gadiraju, A. Bozzon, B. Timmermans, This Is Not What We Ordered: Exploring Why Biased Search Result Rankings Affect User Attitudes on Debated Topics, in: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Informa...

    2021

  28. [38]

    Rieger, T

    A. Rieger, T. Draws, M. Theune, N. Tintarev, Nudges to Mitigate Confirmation Bias during Web Search on Debated Topics: Support vs. Manipulation, ACM Trans. Web 18 (2024) 27:1–27:27. doi:10.1145/3635034

    2024 doi

  29. [39]

    Liu, Deconstructing search tasks in interactive information retrieval: A systematic review of task dimensions and predictors, Information Processing & Management 58 (2021) 102522

    J. Liu, Deconstructing search tasks in interactive information retrieval: A systematic review of task dimensions and predictors, Information Processing & Management 58 (2021) 102522. doi:10.1016/j.ipm.2021.102522

    2021

  30. [40]

    Hienert, M

    D. Hienert, M. Mitsui, P. Mayr, C. Shah, N. J. Belkin, The Role of the Task Topic in Web Search of Different Task Types, in: Proceedings of the 2018 Conference on Human Information Interaction & Retrieval, CHIIR ’18, Association for Computing Machinery, New York, NY, USA, 2018...

    2018

  31. [41]

    H. L. O’Brien, A. Kampen, A. W. Cole, K. Brennan, The Role of Domain Knowledge in Search as Learning, in: Proceedings of the 2020 Conference on Human Information Interaction and Retrieval, CHIIR ’20, Association for Computing Machinery, New York, NY, USA, 2020, pp. 313–

    2020

  32. [42]

    R. W. White, S. T. Dumais, J. Teevan, Characterizing the influence of domain expertise on web search behavior, in: Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM ’09, Association for Computing Machinery, New York, NY, USA, 2009, pp. ...

    2009

  33. [43]

    Al-Samarraie, A

    H. Al-Samarraie, A. Eldenfria, H. Dawoud, The impact of personality traits on users’ information- seeking behavior, Information Processing & Management 53 (2017) 237–247. doi: 10.1016/j. ipm.2016.08.004

    2017 doi

  34. [44]

    Hassoun, I

    A. Hassoun, I. Beacock, S. Consolvo, B. Goldberg, P. G. Kelley, D. M. Russell, Practicing Information Sensibility: How Gen Z Engages with Online Information, in: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, CHI ’23, Association for Computing Ma...

    2023

  35. [46]

    Lorenz-Spreen, S

    P. Lorenz-Spreen, S. Lewandowsky, C. R. Sunstein, R. Hertwig, How behavioural sciences can promote truth, autonomy and democratic discourse online, Nature Human Behaviour 4 (2020) 1102–1109. doi:10.1038/s41562-020-0889-7

    2020 doi

  36. [47]

    Werthner, E

    H. Werthner, E. Prem, E. A. Lee, C. Ghezzi (Eds.), Perspectives on Digital Humanism, Springer Nature, 2022. doi:10.1007/978-3-030-86144-5

    2022 doi

  37. [48]

    Kozyreva, P

    A. Kozyreva, P. Lorenz-Spreen, S. M. Herzog, U. K. H. Ecker, S. Lewandowsky, R. Hertwig, A. Ali, J. Bak-Coleman, S. Barzilai, M. Basol, A. J. Berinsky, C. Betsch, J. Cook, L. K. Fazio, M. Geers, A. M. Guess, H. Huang, H. Larreguy, R. Maertens, F. Panizza, G. Pennycook, D. G. R...

    2024

  38. [49]

    M. Bink, D. Elsweiler, Balancing Act: Boosting Strategies for Informed Search on Controversial Topics, in: Proceedings of the 2024 Conference on Human Information Interaction and Retrieval, CHIIR ’24, Association for Computing Machinery, New York, NY, USA, 2024, pp. 254–265. d...

    2024

  39. [50]

    M. L. Paramita, M. Kasinidou, S. Kleanthous, P. Rosso, T. Kuflik, F. Hopfgartner, Towards improving user awareness of search engine biases: A participatory design approach, Journal of the Association for Information Science and Technology 75 (2024) 581–599. doi:10.1002/asi.24826

    2024 doi

  40. [51]

    P. Ren, Z. Chen, X. Song, B. Li, H. Yang, J. Ma, Understanding temporal intent of user query based on time-based query classification, in: CCF International Conference on Natural Language Processing and Chinese Computing, Springer, 2013, pp. 334–345

    2013

  41. [52]

    Zhang, S

    X. Zhang, S. Han, W. Lu, Automatic prediction of news intent for search queries, The Electronic Library (2018)

    2018

  42. [53]

    A. Sun, M. Hu, Query-guided event detection from news and blog streams, IEEE Transactions on systems, man, and cybernetics-Part A: systems and humans 41 (2011) 834–839

    2011

  43. [54]

    Diaz, Integration of news content into web results, in: Proceedings of the Second ACM International Conference on Web Search and Data Mining, 2009, pp

    F. Diaz, Integration of news content into web results, in: Proceedings of the Second ACM International Conference on Web Search and Data Mining, 2009, pp. 182–191

    2009

  44. [55]

    F. Diaz, J. Arguello, Adaptation of offline vertical selection predictions in the presence of user feedback, in: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, 2009, pp. 323–330

    2009

  45. [56]

    Louis, E

    A. Louis, E. Crestan, Y. Billawala, R. Shen, F. Diaz, J.-F. Crespo, Use of query similarity for improving presentation of news verticals., in: VLDS, 2011, pp. 62–67

    2011

  46. [57]

    Deviatkin, A

    D. Deviatkin, A. Shelmanov, D. Larionov, Discovering novel emergency events in text streams, Proceedings of Data Analytics and Management in Data Intensive Domains (2018) 208–215

    2018

  47. [58]

    Q. Li, A. Nourbakhsh, S. Shah, X. Liu, Real-time novel event detection from social media, in: 2017 IEEE 33Rd international conference on data engineering (ICDE), IEEE, 2017, pp. 1129–1139

    2017

  48. [59]

    Y. Chen, H. Amiri, Z. Li, T.-S. Chua, Emerging topic detection for organizations from microblogs, in: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, 2013, pp. 43–52

    2013

  49. [60]

    J. Wang, L. Li, F. Tan, Y. Zhu, W. Feng, Detecting hotspot information using multi-attribute based topic model, PloS one 10 (2015) e0140539

    2015

  50. [61]

    Cataldi, L

    M. Cataldi, L. Di Caro, C. Schifanella, Emerging topic detection on twitter based on temporal and social terms evaluation, in: The 10th international workshop on multimedia data mining, 2010, pp. 1–10

    2010

  51. [62]

    B.-K. Bao, C. Xu, W. Min, M. S. Hossain, Cross-platform emerging topic detection and elaboration from multimedia streams, ACM Transactions on Multimedia Computing, Communications, and Applications (????)

  52. [63]

    Radinsky, E

    K. Radinsky, E. Horvitz, Mining the web to predict future events, in: Proceedings of the sixth ACM international conference on Web search and data mining, 2013, pp. 255–264

    2013

  53. [64]

    S. P. Kasiviswanathan, P. Melville, A. Banerjee, V. Sindhwani, Emerging topic detection using dictionary learning, in: Proceedings of the 20th ACM international conference on Information and knowledge management, 2011, pp. 745–754

    2011

  54. [65]

    Kasiviswanathan, H

    S. Kasiviswanathan, H. Wang, A. Banerjee, P. Melville, Online l1-dictionary learning with applica- tion to novel document detection, Advances in Neural Information Processing Systems 25 (2012) 2258–2266

    2012

  55. [66]

    S. P. Kasiviswanathan, G. Cong, P. Melville, R. D. Lawrence, Novel document detection for massive data streams using distributed dictionary learning, IBM Journal of Research and Development 57 (2013) 9–1

    2013

  56. [67]

    B. D. Horne, S. Adali, This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news, arXiv preprint arXiv:1703.09398 (2017)

    2017 arXiv

  57. [68]

    K. Shu, D. Mahudeswaran, S. Wang, H. Liu, Hierarchical propagation networks for fake news detection: Investigation and exploitation, in: Proceedings of the International AAAI Conference on Web and Social Media, volume 14, 2020, pp. 626–637

    2020

  58. [69]

    S. S. Ghosal, A. Jurek-Loughrey, et al., Resco-cc: Unsupervised identification of key disinformation sentences, in: The 22nd International Conference on Information Integration and Web-based Applications & Services (iiWAS ’20), 2020

    2020

  59. [70]

    G. B. Guacho, S. Abdali, N. Shah, E. E. Papalexakis, Semi-supervised content-based detection of misinformation via tensor embeddings, in: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEEE, 2018, pp. 322–325

    2018

  60. [71]

    Przybyla, Capturing the style of fake news, in: Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, 2020, pp

    P. Przybyla, Capturing the style of fake news, in: Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, 2020, pp. 490–497

    2020

  61. [72]

    Ghanem, S

    B. Ghanem, S. P. Ponzetto, P. Rosso, F. Rangel, Fakeflow: Fake news detection by modeling the flow of affective information, in: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021, pp. 679–689

    2021

  62. [73]

    Sarker, D

    A. Sarker, D. Mollá, C. Paris, Query-oriented evidence extraction to support evidence-based medicine practice, Journal of biomedical informatics 59 (2016) 169–184

    2016

  63. [74]

    Rudra, S

    K. Rudra, S. Ghosh, N. Ganguly, P. Goyal, S. Ghosh, Extracting situational information from microblogs during disaster events: a classification-summarization approach, in: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, 2015, pp...

    2015

  64. [75]

    Nallapati, B

    R. Nallapati, B. Zhou, C. Gulcehre, B. Xiang, et al., Abstractive text summarization using sequence- to-sequence rnns and beyond, in: Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning, 2016, pp. 280–290

    2016

  65. [76]

    M. Yang, W. Tu, Q. Qu, K. Lei, X. Chen, J. Zhu, Y. Shen, Mares: multitask learning algorithm for web-scale real-time event summarization, World Wide Web 22 (2019) 499–515

    2019

  66. [77]

    Kedzie, F

    C. Kedzie, F. Diaz, K. McKeown, Real-time web scale event summarization using sequential decision making, arXiv preprint arXiv:1605.03664 (2016)

    2016 arXiv

  67. [78]

    M. Zopf, E. L. Mencía, J. Fürnkranz, Sequential clustering and contextual importance measures for incremental update summarization, in: Proceedings of COLING 2016, 2016, pp. 1071–1082

    2016

  68. [79]

    X. Pu, M. Gao, X. Wan, Summarization is (almost) dead, arXiv preprint arXiv:2309.09558 (2023)

    2023 arXiv

  69. [80]

    Zhang, P

    H. Zhang, P. S. Yu, J. Zhang, A systematic survey of text summarization: From statistical methods to large language models, ACM Computing Surveys (2024)

    2024

  70. [81]

    Liu, Toward Cranfield-inspired reusability assessment in interactive information retrieval evaluation, Information Processing & Management 59 (2022) 103007

    J. Liu, Toward Cranfield-inspired reusability assessment in interactive information retrieval evaluation, Information Processing & Management 59 (2022) 103007. doi: 10.1016/j.ipm.2022. 103007

    2022 doi

  71. [317]

    doi:10.1145/3343413.3377989

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.