Pith. sign in

REVIEW 4 major objections 6 minor 154 references

Integrating Zero-Shot Classification to Advance Long COVID Literature: A Systematic Social Media-Centered Review

T0 review · 4 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A zero-shot language model can sort 40 Long COVID social-media studies into four themes without training labels.

desk verdict A usable scoping review of Long COVID social media research, but the zero-shot classification claim is underspecified and unvalidated. read the letter →

arxiv 2412.18779 v1 pith:MP4TORE7 submitted 2024-12-25 cs.SI

Nirmalya Thakur This is my paper · ORCID
classification cs.SI
keywords LongCOVIDzero-shotlearningsocialmediasystematicreviewtransformertextclassificationTwitterReddit
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

This paper tries to establish that a transformer-based zero-shot classifier can organize a systematic review of Long COVID social media research without any manually labeled training set. The author assembled 40 studies that mine Twitter, Reddit, Facebook, and YouTube for Long COVID discourse and asked a pretrained language model to assign each abstract to one of four author-defined themes: symptom characterization, NLP and computational methods, policy and advocacy, or community and support. If the assignments are accurate, the paper would demonstrate a fast, scalable way to map an emerging literature whose taxonomies are still unsettled. The review narrative in Section 4 rests entirely on these automated labels.

What carries the argument

The central mechanism is a transformer-based zero-shot text classifier: a pretrained language model that returns a scalar score $s(\text{text}, k)$ measuring semantic alignment between a study's abstract and each of four category descriptions, followed by a softmax over the scores to give probabilities $p_k(\text{text})$, with the highest-probability category chosen as the label. The paper supplements this with a "carefully curated dictionary-based keyword matching" step that refines the probabilistic output, though the dictionary and the model are not specified. This machinery carries the argument because it, and it alone, produces the Table 1 category assignments that organize the entire Section 4 review.

What would settle it

Concrete check: recruit two independent annotators to assign the same 40 abstracts to the four categories and compute agreement (for example, Cohen's kappa) with Table 1; near-chance agreement would show the zero-shot labels do not reliably organize the literature. A second check: rerun a named zero-shot transformer with the four category descriptions and the stated dictionary, and see whether the resulting assignments diverge substantially from Table 1, which would indicate the reported pipeline cannot be reproduced.

Watch

Extended reading notes

Core claim

The paper's central claim is that zero-shot learning, formalized as scoring an abstract $\text{text}$ against candidate category descriptions $c_1,\dots,c_N$ and normalizing the scores with a softmax, assigns each study to the category with the highest probability, with dictionary-based keyword matching used to refine borderline cases. Applied to 40 included studies, this yields Table 1, in which most studies fall under Clinical or Symptom Characterization, followed by Online Communities and Social Support, Advanced NLP or Computational Methods, and Policy, Advocacy, or Public Health Communication. The paper presents this pipeline as its methodological contribution: a pretrained transformer can categorize research papers without predefined training labels, making literature assessment faster and more scalable in rapidly evolving fields like Long COVID.

Load-bearing premise

The load-bearing premise is that the zero-shot model's category labels, refined by an unspecified dictionary-based keyword step, are accurate enough to organize the review; the paper never checks them against human annotation, inter-rater agreement, or any baseline, so if the labels are wrong the whole thematic structure of the review fails.

Editorial extensions

If this is right

  • A literature review in an emerging health domain can be organized within days of assembling the corpus, without building a labeled dataset.
  • The same four-category pipeline can be applied to other domains with minimal customization effort, as the paper explicitly suggests.
  • If the labels are right, readers get a structured map of where social media evidence exists for clinical characterization, computational methods, policy communication, and patient support.
  • The approach offers a blueprint for future reviews that need to integrate heterogeneous sources of knowledge while devoting time to interpretation rather than labeling.
  • The classification can highlight where the Long COVID social-media literature is concentrated and where it remains thin, guiding future research priorities.

Reading between the lines

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

  • A direct test of the claim would be to have independent human annotators code the same 40 abstracts into the four categories and measure agreement against Table 1; without such a check, the labels are an untested model output rather than an established finding.
  • Because the model and dictionary are not named, the method as described cannot be reproduced as written; specifying them would let other teams verify the assignments and reuse the pipeline.
  • If the approach transfers, literature reviews could become continuously updated dashboards during fast-moving health crises, but transfer would require revalidating the category descriptions for each new condition.
  • The author acknowledges that many studies fit multiple themes, which suggests that a single-label assignment may hide the multidimensional nature of the literature and that a multi-label variant would be a natural extension.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The manuscript presents a systematic review of 40 studies that mine, analyze, or interpret social media content about Long COVID. The author proposes a transformer-based zero-shot classification pipeline to assign each study to one of four categories (Clinical or Symptom Characterization, Advanced NLP or Computational Methods, Policy/Advocacy/Public Health Communication, and Community and Social Support), and then reviews the studies under these categories. The core claims are that the zero-shot approach is a novel methodological contribution and that it enables rapid, scalable literature categorization without labeled training data.

Significance. If the classification approach were fully specified and validated, the paper would offer a useful demonstration of zero-shot text classification for organizing emerging interdisciplinary literature, and the compiled set of 40 social-media Long COVID studies would be a helpful resource. The paper is honest about some limitations and makes an effort to structure the review around interpretable themes. However, the methodological novelty is the central selling point, and it is currently not supported by the evidence presented; the review's organization is also internally inconsistent. The significance is therefore mostly potential rather than demonstrated.

major comments (4)
  1. [§2–§3, Eqs. (1)–(3)] The zero-shot classification pipeline is not specified in a way that allows reproduction or evaluation. Equations (1)–(3) are generic definitions of scoring and softmax normalization; the concrete model (e.g., which transformer checkpoint), the prompt templates, the exact category descriptions, and the fusion rule with the unspecified 'carefully curated dictionary-based keyword matching' are never given. Since the abstract and Section 3 both present this pipeline as a 'novel' contribution, the central methodological claim cannot be checked as written.
  2. [§3, Table 1] No gold-standard evaluation of the classification is reported. There is no comparison with human annotations, no inter-rater agreement, no baseline method, and no ablation separating the zero-shot component from the dictionary component. The only limitation statement in Section 5 mentions vocabulary nuance rather than label accuracy, so the assertion in Section 3 that the pipeline is 'robust and interpretable' is unsupported.
  3. [Table 1 vs. §4.1 and §4.3] The review text does not consistently follow the labels in Table 1. Papers [82] and [78] are labeled 'Symptom Characterization' in Table 1 but are discussed under 'NLP and Modeling' in Section 4.1, and [94]—whose title and Section 4.3 description emphasize public health communication and vaccine hesitancy—is classified as 'Community and Support.' This inconsistency undermines the claim that the zero-shot labels organize the review structure.
  4. [§2 and Table 1] The corpus of '40 studies' contains duplicate records of the same work: [68]/[95], [72]/[89], and [67]/[90] are preprint/published versions of the same studies, and [66]/[92] appear to be the same Twitter symptom analysis. The number of unique papers is therefore unclear, which affects the integrity of the systematic-review count and the per-paper discussion in Section 4.
minor comments (6)
  1. [§1] The word 'bromyalgia' is a typo for 'fibromyalgia'.
  2. [§2] The search strategy does not report database-specific query strings, the date the search was executed, or a PRISMA-style flow diagram, which are standard for a systematic review.
  3. [§3] The statement that 'a program was written in Python 3.10' is not accompanied by a code repository or implementation details; providing the code or a link would materially aid reproducibility.
  4. [Table 1] Some rows do not list a full author list despite the column header 'Full Author List'; for example, rows [79] and [93] show only titles.
  5. [Abstract and §2] The platform name is used inconsistently: the abstract and some body passages say 'X (formerly Twitter),' while other parts simply say 'Twitter.'
  6. [References] Reference [141] lacks a period after the reference number in the bibliography list.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the zero-shot classification outputs are computed from the abstracts, not re-used inputs; the paper's validity gaps are not circularity.

full rationale

Walking the derivation chain, the paper defines four thematic categories, applies a generic transformer-based zero-shot scorer to each abstract (Equations 1-3), obtains a label per paper, and then organizes Section 4 around those labels. The labels are outputs of the stated procedure, not inputs that are renamed as predictions, so there is no self-definitional or fitted-input circularity. The dictionary-based refinement is mentioned but never specified, which makes the pipeline unreproducible, but without the dictionary text or fusion equations one cannot exhibit an equation-level reduction to the author's category definitions. The many self-citations are background references and are not load-bearing for the zero-shot classification claim, which rests on the cited zero-shot literature and the abstracts themselves. Duplicate entries and the absence of human-annotation or baseline validation are correctness and rigor concerns, not circularity. Therefore no circular step meets the evidence standard.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claim rests on three unvalidated assumptions: that a pretrained transformer can classify abstracts into the author's categories, that the four categories themselves are a valid partition, and that the unnamed dictionary-based refinement improves the labels. There are no numeric free parameters because no fitting is reported, but the category descriptions and dictionary are hand-chosen by the author and not externally benchmarked.

assumptions (3)
  • domain assumption A pretrained transformer's semantic scoring is sufficient for assigning papers to the four categories.
    Section 2, Equations 1 through 3 rely on the model's pretrained representations to compute alignment, but no validation is performed against human labels.
  • ad hoc to paper The four author-defined categories are a meaningful and exhaustive partition for the included papers.
    The categories are defined in Section 2 from scratch, with no external taxonomy, inter-rater agreement, or test of exclusivity and coverage.
  • ad hoc to paper Dictionary-based keyword matching, when combined with zero-shot scores, improves or confirms the final labels.
    Section 3 states that this synergy made the pipeline more robust, but the dictionary, the keywords, and the combination rule are never described.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Integrating Zero-Shot Classification to Advance Long COVID Literature: A Systematic Social Media-Centered Review." pith.science (2026). https://pith.science/paper/MP4TORE7

@misc{pith2026241218779,
  author       = {Pith},
  title        = {Pith review of: Integrating Zero-Shot Classification to Advance Long COVID Literature: A Systematic Social Media-Centered Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MP4TORE7}},
  note         = {Machine review of arXiv:2412.18779}
}
read the original abstract

Long COVID continues to challenge public health by affecting a significant segment of individuals who have recovered from acute SARS-CoV-2 infection yet endure prolonged and often debilitating symptoms. Social media has emerged as a vital resource for those seeking real-time information, peer support, and validating their health concerns related to Long COVID. This paper examines recent works focusing on mining, analyzing, and interpreting user-generated content on social media platforms such as Twitter, Reddit, Facebook, and YouTube to capture the broader discourse on persistent post-COVID conditions. A novel transformer-based zero-shot learning approach serves as the foundation for classifying research papers in this area into four primary categories: Clinical or Symptom Characterization, Advanced NLP or Computational Methods, Policy, Advocacy, or Public Health Communication, and Online Communities and Social Support. This methodology showcases the adaptability of advanced language models in categorizing research papers without predefined training labels, thus enabling a more rapid and scalable assessment of existing literature. This review highlights the multifaceted nature of Long COVID research, where computational techniques applied to social media data reveal insights into narratives of individuals suffering from Long COVID. This review also demonstrates the capacity of social media analytics to inform clinical practice and contribute to policy-making related to Long COVID.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

154 extracted references · 98 canonical work pages

  1. [68]

    https://doi.org/10.1101/2021.06.15.21259004

    Sarker, A., Ge, Y.: Long COVID symptoms from Reddit: Characterizing post -COVID syndrome from patient reports, http://dx.doi.org/10.1101/2021.06.15.21259004, (2021). https://doi.org/10.1101/2021.06.15.21259004

    work page doi:10.1101/2021.06.15.21259004 2021

  2. [95]

    JAMIA Open

    Sarker, A., Ge, Y.: Mining long -COVID symptoms from Reddit: characterizing post - COVID syndrome from patient reports. JAMIA Open. 4, (2021). https://doi.org/10.1093/jamiaopen/ooab075

    work page doi:10.1093/jamiaopen/ooab075 2021

  3. [72]

    https://doi.org/10.1101/2022.12.14.22283419

    Dolatabadi, E., Moyano, D., Bales, M., Spasojevic, S., Bhambhoria, R., Bhatti, J., Debnath, S., Hoell, N., Li, X., Leng, C., Nanda, S., Saab, J., Sahak, E., Sie, F., Uppal, S., Vadlamudi, N.K., Vladimirova, A., Yakimovich, A., Yang, X., Kocak, S.A., Cheung , 25 A.M.: Using social media to help understand long COVID patient reported health out- comes: A na...

    work page doi:10.1101/2022.12.14.22283419 2022

  4. [89]

    Dolatabadi, E., Moyano, D., Bales, M., Spasojevic, S., Bhambhoria, R., Bhatti, J., Debnath, S., Hoell, N., Li, X., Leng, C., Nanda, S., Saab, J., Sahak, E., Sie, F., Uppal, S., Vadlamudi, N.K., Vladimirova, A., Yakimovich, A., Yang, X., Kocak, S.A., Cheung , A.M.: Using social media to help understand patient -reported health outcomes of post – COVID-19 c...

    work page doi:10.2196/45767 2023

  5. [67]

    https://doi.org/10.1101/2021.03.21.21253968

    Ziauddeen, N., Gurdasani, D., O’Hara, M.E., Hastie, C., Roderick, P., Yao, G., Alwan, N.A.: Characteristics of Long Covid: findings from a social media survey, http://dx.doi.org/10.1101/2021.03.21.21253968, (2021). https://doi.org/10.1101/2021.03.21.21253968

    work page doi:10.1101/2021.03.21.21253968 2021

  6. [90]

    PLoS One

    Ziauddeen, N., Gurdasani, D., O’Hara, M.E., Hastie, C., Roderick, P., Yao, G., Alwan, N.A.: Characteristics and impact of Long Covid: Findings from an online survey. PLoS One. 17, e0264331 (2022). https://doi.org/10.1371/journal.pone.0264331

    work page doi:10.1371/journal.pone.0264331 2022

  7. [82]

    JMIR Form

    Koss, J., Bohnet -Joschko, S.: Social media mining of long -COVID self-medication re- ported by Reddit users: Feasibility study to support drug repurposing. JMIR Form. Res. 6, e39582 (2022). https://doi.org/10.2196/39582

    work page doi:10.2196/39582 2022

  8. [78]

    Awoyemi, T., Ebili, U., Olusanya, A., Ogunniyi, K.E., Adejumo, A.V.: Twitter sentiment analysis of long COVID syndrome. Cureus. 14, e25901 (2022). https://doi.org/10.7759/cu- reus.25901

    work page doi:10.7759/cu- 2022

  9. [94]

    Miyake, E., Martin, S.: Long Covid: Online patient narratives, public health communica- tion and vaccine hesitancy. Digit. Health. 7, (2021). https://doi.org/10.1177/20552076211059649

    work page doi:10.1177/20552076211059649 2021

  10. [66]

    https://doi.org/10.1101/2020.08.14.20175059

    Singh, S.M., Reddy, C.: An analysis of self -reported longcovid symptoms on twitter, http://dx.doi.org/10.1101/2020.08.14.20175059, (2020). https://doi.org/10.1101/2020.08.14.20175059

    work page doi:10.1101/2020.08.14.20175059 2020

  11. [92]

    Singh, S.M., Reddy, S.C., Kathiravan, S.: An analysis of self -reported long COVID -19 symptoms on twitter. J. Postgrad. Med. Educ. Res. 57, 79 –81 (2023). https://doi.org/10.5005/jp-journals-10028-1616

    work page doi:10.5005/jp-journals-10028-1616 2023

Show all 154 references
  1. [1]

    -C., Wang, C

    Ciotti, M., Ciccozzi, M., Terrinoni, A., Jiang, W. -C., Wang, C. -B., Bernardini, S.: The COVID-19 pandemic. Crit. Rev. Clin. Lab. Sci. 57, 365 –388 (2020). https://doi.org/10.1080/10408363.2020.1783198

    2020

  2. [2]

    Velavan, T.P., Meyer, C.G.: The COVID‐19 epidemic. Trop. Med. Int. Health. 25, 278 – 280 (2020). https://doi.org/10.1111/tmi.13383

    2020 doi

  3. [3]

    Infection

    Yesudhas, D., Srivastava, A., Gromiha, M.M.: COVID-19 outbreak: history, mechanism, transmission, structural studies and therapeutics. Infection. 49, 199 –213 (2021). https://doi.org/10.1007/s15010-020-01516-2

    2021 doi

  4. [4]

    Allen, D.W.: Covid-19 lockdown cost/benefits: A critical assessment of the literature. Int. J. Econ. Bus. 29, 1–32 (2022). https://doi.org/10.1080/13571516.2021.1976051

    2022

  5. [5]

    Acta Biomed

    Cucinotta, D., Vanelli, M.: WHO Declares COVID-19 a Pandemic. Acta Biomed. Ateneo Parmense. 91, 157–160 (2020). https://doi.org/10.23750/abm.v91i1.9397

    2020 doi

  6. [6]

    COVID-19 cases, https://covid19.who.int/, last accessed 2024/12/23

    2024

  7. [7]

    Diabetes Metab

    Raveendran, A.V., Jayadevan, R., Sashidharan, S.: Long COVID: An overview. Diabetes Metab. Syndr. 15, 869–875 (2021). https://doi.org/10.1016/j.dsx.2021.04.007

    2021 doi

  8. [8]

    Altmann, D.M., Whettlock, E.M., Liu, S., Arachchillage, D.J., Boyton, R.J.: The immu- nology of long COVID. Nat. Rev. Immunol. 23, 618 –634 (2023). https://doi.org/10.1038/s41577-023-00904-7

    2023 doi

  9. [9]

    Infection

    Fernández-de-las-Peñas, C.: Long COVID: current definition. Infection. 50, 285 –286 (2022). https://doi.org/10.1007/s15010-021-01696-5

    2022 doi

  10. [10]

    CDC: Long COVID basics, https://www.cdc.gov/covid/long-term-effects/index.html, last accessed 2024/12/23

    2024

  11. [11]

    https://doi.org/10.17226/27768

    Committee on Examining the Working Definition for Long COVID, Board on Health Sci- ences Policy, Board on Global Health, Health and Medicine Division, National Academies of Sciences, Engineering, and Medicine: A long COVID definition: A chronic, systemic disease state with pro...

    2024 doi

  12. [12]

    Aiyegbusi, O.L., Hughes, S.E., Turner, G., Rivera, S.C., McMullan, C., Chandan, J.S., Haroon, S., Price, G., Davies, E.H., Nirantharakumar, K., Sapey, E., Calvert, M.J., on be- half of the TLC Study Group: Symptoms, complications and management of long COVID: 21 a review. J. R...

    2021 doi

  13. [13]

    Subramanian, A., Nirantharakumar, K., Hughes, S., Myles, P., Williams, T., Gokhale, K.M., Taverner, T., Chandan, J.S., Brown, K., Simms -Williams, N., Shah, A.D., Singh, M., Kidy, F., Okoth, K., Hotham, R., Bashir, N., Cockburn, N., Lee, S.I., Turner, G.M., Gkoutos, G.V., Aiye...

    2022

  14. [14]

    Sudre, C.H., Murray, B., Varsavsky, T., Graham, M.S., Penfold, R.S., Bowyer, R.C., Pu- jol, J.C., Klaser, K., Antonelli, M., Canas, L.S., Molteni, E., Modat, M., Jorge Cardoso, M., May, A., Ganesh, S., Davies, R., Nguyen, L.H., Drew, D.A., Astley, C.M., Joshi, A.D., Merino, J....

    2021

  15. [15]

    EClinicalMedi- cine

    Notarte, K.I., Catahay, J.A., Velasco, J.V., Pastrana, A., Ver, A.T., Pangilinan, F.C., Peligro, P.J., Casimiro, M., Guerrero, J.J., Gellaco, M.M.L., Lippi, G., Henry, B.M., Fer- nández-de-las-Peñas, C.: Impact of COVID-19 vaccination on the risk of developing long- COVID and ...

    2022

  16. [16]

    Cabrera Martimbianco, A.L., Pacheco, R.L., Bagattini, Â.M., Riera, R.: Frequency, signs and symptoms, and criteria adopted for long COVID‐19: A systematic review. Int. J. Clin. Pract. 75, (2021). https://doi.org/10.1111/ijcp.14357

    2021 doi

  17. [17]

    Ayoubkhani, D., Bermingham, C., Pouwels, K.B., Glickman, M., Nafilyan, V., Zaccardi, F., Khunti, K., Alwan, N.A., Walker, A.S.: Trajectory of long covid symptoms after covid- 19 vaccination: community based cohort study. BMJ. 377, e069676 (2022). https://doi.org/10.1136/bmj-20...

    2022 doi

  18. [18]

    EClinicalMedicine

    Davis, H.E., Assaf, G.S., McCorkell, L., Wei, H., Low, R.J., Re’em, Y., Redfield, S., Aus- tin, J.P., Akrami, A.: Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine. 38, 101019 (2021). https://doi.org/10.1016/j.eclinm...

    2021

  19. [19]

    Yong, S.J.: Long COVID or post -COVID-19 syndrome: putative pathophysiology, risk factors, and treatments. Infect. Dis. (Lond.). 53, 737 –754 (2021). https://doi.org/10.1080/23744235.2021.1924397

    2021

  20. [20]

    Koc, H.C., Xiao, J., Liu, W., Li, Y., Chen, G.: Long COVID and its management. Int. J. Biol. Sci. 18, 4768–4780 (2022). https://doi.org/10.7150/ijbs.75056

    2022 doi

  21. [21]

    Al-Aly, Z., Davis, H., McCorkell, L., Soares, L., Wulf -Hanson, S., Iwasaki, A., Topol, E.J.: Long COVID science, research and policy. Nat. Med. 30, 2148 –2164 (2024). https://doi.org/10.1038/s41591-024-03173-6

    2024 doi

  22. [22]

    -J., Harriott, A.M., García -Azorín, D., Labastida - Ramirez, A., Ornello, R., Raffaelli, B., Beltrán, E.R., Ruscheweyh, R., Martelletti, P.: Long COVID headache

    Tana, C., Bentivegna, E., Cho, S. -J., Harriott, A.M., García -Azorín, D., Labastida - Ramirez, A., Ornello, R., Raffaelli, B., Beltrán, E.R., Ruscheweyh, R., Martelletti, P.: Long COVID headache. J. Headache Pain. 23, (2022). https://doi.org/10.1186/s10194 - 022-01450-8

    2022 doi

  23. [23]

    Peluso, M.J., Deeks, S.G.: Mechanisms of long COVID and the path toward therapeutics. Cell. 187, 5500–5529 (2024). https://doi.org/10.1016/j.cell.2024.07.054

    2024 doi

  24. [24]

    Greenhalgh, T., Sivan, M., Perlowski, A., Nikolich, J.Ž.: Long COVID: a clinical update. Lancet. 404, 707–724 (2024). https://doi.org/10.1016/s0140-6736(24)01136-x

    2024 doi

  25. [25]

    199, 113–119 (2021)

    Sykes, D.L., Holdsworth, L., Jawad, N., Gunasekera, P., Morice, A.H., Crooks, M.G.: Post-COVID-19 symptom burden: What is long -COVID and how should we manage it? Lung. 199, 113–119 (2021). https://doi.org/10.1007/s00408-021-00423-z. 22

    2021 doi

  26. [26]

    Lancet Digit

    Tsao, S.-F., Chen, H., Tisseverasinghe, T., Yang, Y., Li, L., Butt, Z.A.: What social media told us in the time of COVID -19: a scoping review. Lancet Digit. Health. 3, e175 –e194 (2021). https://doi.org/10.1016/s2589-7500(20)30315-0

    2021 doi

  27. [27]

    Infor- mation (Basel)

    Thakur, N.: Social media mining and analysis: A brief review of recent challenges. Infor- mation (Basel). 14, 484 (2023). https://doi.org/10.3390/info14090484

    2023 doi

  28. [28]

    Gottlieb, M., Dyer, S.: Information and disinformation: Social media in the COVID‐19 crisis. Acad. Emerg. Med. 27, 640–641 (2020). https://doi.org/10.1111/acem.14036

    2020 doi

  29. [29]

    Thakur, N., Han, C.: An exploratory study of tweets about the SARS -CoV-2 Omicron variant: Insights from sentiment analysis, language interpretation, source tracking, type classification, and embedded URL detection. COVID. 2, 1026 –1049 (2022). https://doi.org/10.3390/covid2080076

    2022 doi

  30. [30]

    Int J Front Sci

    Hussain, W.: Role of social media in COVID -19 pandemic. Int J Front Sci. 4, (2024). https://doi.org/10.37978/tijfs.v4i2.144

    2024 doi

  31. [31]

    Big Data Cogn

    Thakur, N.: Sentiment analysis and text analysis of the public discourse on Twitter about COVID-19 and MPox. Big Data Cogn. Comput. 7, 116 (2023). https://doi.org/10.3390/bdcc7020116

    2023 doi

  32. [32]

    Acta Inform

    Shoaei, M.D., Dastani, M.: The role of Twitter during the COVID-19 crisis: A systematic literature review. Acta Inform. Pragensia. 9, 154 –169 (2020). https://doi.org/10.18267/j.aip.138

    2020 doi

  33. [33]

    Computers

    Thakur, N., Cui, S., Khanna, K., Knieling, V., Duggal, Y.N., Shao, M.: Investigation of the gender-specific discourse about online learning during COVID -19 on Twitter using sentiment analysis, subjectivity analysis, and toxicity analysis. Computers. 12, 221 (2023). https://do...

    2023 doi

  34. [34]

    Southwick, L., Guntuku, S.C., Klinger, E.V., Seltzer, E., McCalpin, H.J., Merchant, R.M.: Characterizing COVID-19 content posted to TikTok: Public sentiment and response dur- ing the first phase of the COVID -19 pandemic. J. Adolesc. Health. 69, 234 –241 (2021). https://doi.or...

    2021 doi

  35. [35]

    In: Communications in Computer and Information Science

    Patel, K.A., Thakur, N.: Dissemination of misinformation about COVID-19 on TikTok: A multimodal analysis. In: Communications in Computer and Information Science. pp. 109–

  36. [36]

    Rovetta, A., Bhagavathula, A.S.: Global infodemiology of COVID-19: Analysis of Google web searches and Instagram hashtags. J. Med. Internet Res. 22, e20673 (2020). https://doi.org/10.2196/20673

    2020 doi

  37. [37]

    In: 2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP)

    Thakur, N.: Five years of COVID-19 discourse on Instagram: A labeled Instagram dataset of over half a million posts for multilingual sentiment analysis. In: 2024 7th International Conference on Machine Learning and Natural Language Processing (MLNLP). pp. 1–10. IEEE (2024)

    2024

  38. [38]

    In: Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies

    Mejova, Y., Kalimeri, K.: COVID-19 on Facebook ads: Competing agendas around a pub- lic health crisis. In: Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies. pp. 22–31. ACM, New York, NY, USA (2020)

    2020

  39. [39]

    EPJ Data Sci

    Perrotta, D., Grow, A., Rampazzo, F., Cimentada, J., Del Fava, E., Gil-Clavel, S., Zagheni, E.: Behaviours and attitudes in response to the COVID-19 pandemic: insights from a cross- national Facebook survey. EPJ Data Sci. 10, 17 (2021). https://doi.org/10.1140/epjds/s13688-021-00270-1

    2021 doi

  40. [40]

    -Y., Bailey, A., Huynh, D., Chan, J.: YouTube as a source of information on COVID-19: a pandemic of misinformation? BMJ Glob

    Li, H.O. -Y., Bailey, A., Huynh, D., Chan, J.: YouTube as a source of information on COVID-19: a pandemic of misinformation? BMJ Glob. Health. 5, e002604 (2020). https://doi.org/10.1136/bmjgh-2020-002604

    2020 doi

  41. [41]

    Computation (Basel)

    Thakur, N., Cui, S., Knieling, V., Khanna, K., Shao, M.: Investigation of the misinfor- mation about COVID-19 on YouTube using topic modeling, sentiment analysis, and lan- guage analysis. Computation (Basel). 12, 28 (2024). https://doi.org/10.3390/computa- tion12020028. 23

    2024 doi

  42. [42]

    Proceedings of the Interna- tional AAAI Conference on Web and Social Media

    Veselovsky, V., Anderson, A.: Reddit in the time of COVID. Proceedings of the Interna- tional AAAI Conference on Web and Social Media. 17, 878 –889 (2023). https://doi.org/10.1609/icwsm.v17i1.22196

    2023 doi

  43. [43]

    Nirmalya, T., Kesha, A.P., Audrey, P., Shuqi, C., Nazif, A., Rishika, S., Riyan, S.: Quan- tifying public response to COVID -19 events: Introducing the Community Sentiment and Engagement Index, http://arxiv.org/abs/2412.16925, (2024)

    2024 arXiv

  44. [44]

    dynamic adaptations in healthcare during the COVID -19 pandemic

    Daglis, T., Tsagarakis, K.P.: A LinkedIn -based analysis of the U.S. dynamic adaptations in healthcare during the COVID -19 pandemic. Healthcare Analytics. 5, 100291 (2024). https://doi.org/10.1016/j.health.2023.100291

    2024

  45. [45]

    Pardim, V.I., Pinochet, L.H.C., Souza, C.A., Viana, A.B.N.: The behavior of young people at the beginning of their career through LinkedIn. RAM Rev. Adm. Mackenzie. 23, eRAMG220064 (2022). https://doi.org/10.1590/1678-6971/eramg220064.en

    2022 doi

  46. [46]

    Hinchey, L., Michon, A., Drews, J., Price, M., Christian, J., Pernice, F., Aquila, R.: Club- houses as essential communities during the COVID -19 pandemic. J. Psychosoc. Rehabil. Ment. Health. 9, 149–157 (2022). https://doi.org/10.1007/s40737-021-00242-8

    2022 doi

  47. [47]

    Community Ment

    Junaid, S., Mutschler, C., McShane, K., The Canadian Clubhouse Research Group: The impact of COVID -19 on clubhouse employment programs. Community Ment. Health J. 59, 523–530 (2023). https://doi.org/10.1007/s10597-022-01036-3

    2023 doi

  48. [48]

    Ayob, M.A., Hadi, N.A., Ezad, M., Pahroraji, H.M., Ismail, B., Saaid, M.N.F.: Promoting ‘Discord’ as a platform for learning engagement during Covid-19 pandemic. Asian J. Univ. Educ. 18, 663–673 (2022). https://doi.org/10.24191/ajue.v18i3.18953

    2022 doi

  49. [49]

    Journal of English Teaching, Lit- erature, and Applied Linguistics

    Ardiyansah, T.Y., Batubara, R.W., Auliya, P.K.: Using discord to facilitate students in teaching learning process during COVID -19 outbreak. Journal of English Teaching, Lit- erature, and Applied Linguistics. 5, 76 (2021). https://doi.org/10.30587/jetlal.v5i1.2528

    2021 doi

  50. [50]

    Proceedings of the International AAAI Conference on Web and Social Media

    Yang, Q., Wang, W., Pierce, L., Vaish, R., Shi, X., Shah, N.: Online communication shifts in the midst of the Covid -19 pandemic: A case study on Snapchat. Proceedings of the International AAAI Conference on Web and Social Media. 15, 830 –840 (2021). https://doi.org/10.1609/ic...

    2021 doi

  51. [51]

    Spieler, B., Batte, C., Mackey, D., Henry, C., Danrad, R., Sabottke, C., Pirtle, C., Mussell, J., Wallace, E.: Diagnosis in a snap: a pilot study using Snapchat in radiologic didactics. Emerg. Radiol. 28, 93–102 (2021). https://doi.org/10.1007/s10140-020-01825-x

    2021 doi

  52. [52]

    Yue, Z., Zhang, R., Xiao, J.: Social media use, perceived social support, and well -being: Evidence from two waves of surveys peri - and post-COVID-19 lockdown. J. Soc. Pers. Relat. 41, 1279–1297 (2024). https://doi.org/10.1177/02654075231188185

    2024 doi

  53. [53]

    Computers

    Thakur, N., Duggal, Y.N., Liu, Z.: Analyzing public reactions, perceptions, and attitudes during the MPox outbreak: Findings from Topic Modeling of Tweets. Computers. 12, 191 (2023). https://doi.org/10.3390/computers12100191

    2023 doi

  54. [54]

    Thakur, N.: MonkeyPox2022Tweets: A large-scale Twitter dataset on the 2022 Monkey- pox outbreak, findings from analysis of Tweets, and open research questions. Infect. Dis. Rep. 14, 855–883 (2022). https://doi.org/10.3390/idr14060087

    2022 doi

  55. [55]

    Perego, E.: #LongCovid, https://twitter.com/elisaperego78/sta- tus/1263172084055838721?s=20, last accessed 2024/12/23

    2024

  56. [56]

    In: Lecture Notes in Computer Science

    Thakur, N., Cho, H., Cheng, H., Lee, H.: Analysis of user diversity-based patterns of pub- lic discourse on twitter about mental health in the context of online learning during COVID-19. In: Lecture Notes in Computer Science. pp. 367 –389. Springer Nature Swit- zerland, Cham (2023)

    2023

  57. [57]

    infodemics

    Schillinger, D., Chittamuru, D., Ramírez, A.S.: From “infodemics” to health promotion: A novel framework for the role of social media in public health. Am. J. Public Health. 110, 1393–1396 (2020). https://doi.org/10.2105/ajph.2020.305746

    2020

  58. [58]

    Future Internet

    Thakur, N., Patel, K.A., Poon, A., Shah, R., Azizi, N., Han, C.: A comprehensive analysis and investigation of the public discourse on twitter about exoskeletons from 2017 to 2023. Future Internet. 15, 346 (2023). https://doi.org/10.3390/fi15100346. 24

    2023 doi

  59. [59]

    -Z., Wu, Q.M.J.: A review of generalized zero -shot learning methods

    Pourpanah, F., Abdar, M., Luo, Y., Zhou, X., Wang, R., Lim, C.P., Wang, X. -Z., Wu, Q.M.J.: A review of generalized zero -shot learning methods. IEEE Trans. Pattern Anal. Mach. Intell. 45, 1–20 (2022). https://doi.org/10.1109/tpami.2022.3191696

    2022

  60. [60]

    Romera-Paredes, B., Torr, P.H.S.: An embarrassingly simple approach to zero-shot learn- ing. ICML. 37, 2152 –2161 (07--09 Jul 2015). https://doi.org/10.1007/978 -3-319-50077- 5_2

    2015 doi

  61. [61]

    ACM Trans

    Wang, W., Zheng, V.W., Yu, H., Miao, C.: A survey of zero-shot learning: Settings, meth- ods, and applications. ACM Trans. Intell. Syst. Technol. 10, 1 –37 (2019). https://doi.org/10.1145/3293318

    2019 doi

  62. [62]

    Fu, Y.: Investigating public perceptions regarding the Long COVID on Twitter using sen- timent analysis and topic modeling. Med. Data Min. (2022). https://doi.org/10.53388/mdm20220520024

    2022 doi

  63. [63]

    Rushforth, A., Ladds, E., Wieringa, S., Taylor, S., Husain, L., Greenhalgh, T.: Long Covid – The illness narratives. Soc. Sci. Med. 286, 114326 (2021). https://doi.org/10.1016/j.socscimed.2021.114326

    2021

  64. [64]

    -A.D., Schwartz, T., Tumminello, C.M., Bouldin, E.: Support amid uncertainty: Long COVID illness experiences and the role of online com- munities

    Russell, D., Spence, N.J., Chase, J. -A.D., Schwartz, T., Tumminello, C.M., Bouldin, E.: Support amid uncertainty: Long COVID illness experiences and the role of online com- munities. SSM Qual. Res. Health. 2, 100177 (2022). https://doi.org/10.1016/j.ssmqr.2022.100177

    2022

  65. [65]

    https://doi.org/10.13136/2281-4582/2024.I23.1450

    Meledandri, F.: The impact of polarised social media networking communications in the #longcovid debate between ideologies and scientific facts, http://dx.doi.org/10.13136/2281-4582/2024.I23.1450, (2024). https://doi.org/10.13136/2281-4582/2024.I23.1450

    2024 doi

  66. [69]

    Banda, J.M., Adderley, N., Ahmed, W. -U.-R., AlGhoul, H., Alser, O., Alser, M., Areia, C., Cogenur, M., Fišter, K., Gombar, S., Huser, V., Jonnagaddala, J., Lai, L.Y.H., Leis, A., Mateu, L., Mayer, M.A., Minty, E., Morales, D., Natarajan, K., Paredes, R., Periyakoil, V.S., Pra...

    2021

  67. [70]

    https://doi.org/10.1101/2021.07.23.21261030

    Massey, D., Berrent, D., Krumholz, H.: Breakthrough symptomatic COVID-19 infections leading to Long Covid: Report from Long Covid Facebook group poll, http://dx.doi.org/10.1101/2021.07.23.21261030, (2021). https://doi.org/10.1101/2021.07.23.21261030

    2021 doi

  68. [71]

    #LongCOVID af- fects children too

    Martin, S., Chepo, M., Déom, N., Khalid, A.F., Vindrola -Padros, C.: “#LongCOVID af- fects children too”: A Twitter analysis of healthcare workers’ sentiment and discourse about Long COVID in children and young people in the UK, http://dx.doi.org/10.1101/2022.07.20.22277865, (...

    2022 doi

  69. [73]

    In: Hruschka, E., Mitchell, T., Mladenic, D., Grobelnik, M., and Bhutani, N

    Miao, L., Last, M., Litvak, M.: An interactive analysis of user-reported long COVID symp- toms using twitter data. In: Hruschka, E., Mitchell, T., Mladenic, D., Grobelnik, M., and Bhutani, N. (eds.) Proceedings of the 2nd Workshop on Deriving Insights from User-Gen- erated Tex...

    2022

  70. [74]

    Guocheng, F., Huaiyu, C., Wei, Q.: Exploring the emotional and mental well -being of individuals with Long COVID through twitter analysis, http://arxiv.org/abs/2307.07558, (2023)

    2023 arXiv

  71. [75]

    Jordan, A., Park, A.: Understanding the long haulers of COVID-19: Mixed methods anal- ysis of YouTube content. JMIR AI. 3, e54501 (2024). https://doi.org/10.2196/54501

    2024 doi

  72. [76]

    Kusuma, I.Y., Suherman, S.: The pulse of long COVID on Twitter: A social network anal- ysis. Arch. Iran. Med. 27, 36–43 (2024). https://doi.org/10.34172/aim.2024.06

    2024 doi

  73. [77]

    Thakur, N.: Investigating and analyzing self-reporting of Long COVID on Twitter: Find- ings from sentiment analysis. Appl. Syst. Innov. 6, 92 (2023). https://doi.org/10.3390/asi6050092

    2023 doi

  74. [79]

    DIGITAL LONG-HAULER LIFELINES: UNDERSTANDING HOW PEOPLE WITH LONG COVID BUILD COMMUNITY ON REDDIT, https://www.researchgate.net/pub- lication/385720439_Digital_Long-Hauler_Lifelines_Understanding_How_Peo- ple_with_Long_Covid_Build_Community_on_Reddit, last accessed 2024/12/24

    2024

  75. [80]

    Bhattacharyya, A., Seth, A., Rai, S.: The effects of long COVID -19, its severity, and the need for immediate attention: Analysis of clinical trials and Twitter data. Front. Big Data. 5, (2022). https://doi.org/10.3389/fdata.2022.1051386

    2022

  76. [81]

    JMIR Form

    Matharaarachchi, S., Domaratzki, M., Katz, A., Muthukumarana, S.: Discovering long COVID symptom patterns: Association rule mining and sentiment analysis in social media tweets. JMIR Form. Res. 6, e37984 (2022). https://doi.org/10.2196/37984

    2022 doi

  77. [83]

    Ayadi, H., Bour, C., Fischer, A., Ghoniem, M., Fagherazzi, G.: The Long COVID experi- ence from a patient’s perspective: a clustering analysis of 27,216 Reddit posts. Front. Pub- lic Health. 11, (2023). https://doi.org/10.3389/fpubh.2023.1227807

    2023

  78. [84]

    JMIR Hum

    Garrett, C., Aghaei, A., Aggarwal, A., Qiao, S.: The role of social media in the experiences of COVID -19 among long -hauler women: Qualitative study. JMIR Hum. Factors. 11, e50443 (2024). https://doi.org/10.2196/50443

    2024 doi

  79. [85]

    Laestadius, L.I., Guidry, J.P.D., Bishop, A., Campos-Castillo, C.: State health department communication about long COVID in the United States on Facebook: Risks, prevention, and support. Int. J. Environ. Res. Public Health. 19, 5973 (2022). https://doi.org/10.3390/ijerph19105973

    2022 doi

  80. [86]

    Izquierdo-Condoy, J.S., Fernandez -Naranjo, R., Vasconez -González, E., Cordovez, S., Tello-De-la-Torre, A., Paz, C., Delgado -Moreira, K., Carrington, S., Viscor, G., Ortiz - Prado, E.: Long COVID at different altitudes: A countrywide epidemiological analysis. Int. J. Environ...

    2022 doi

  81. [87]

    JMIR Infodemiology

    Santarossa, S., Rapp, A., Sardinas, S., Hussein, J., Ramirez, A., Cassidy -Bushrow, A.E., Cheng, P., Yu, E.: Understanding the #longCOVID and #longhaulers conversation on 26 Twitter: Multimethod study. JMIR Infodemiology. 2, e31259 (2022). https://doi.org/10.2196/31259

    2022 doi

  82. [88]

    JMIR Infodemiology

    Déguilhem, A., Malaab, J., Talmatkadi, M., Renner, S., Foulquié, P., Fagherazzi, G., Lous- sikian, P., Marty, T., Mebarki, A., Texier, N., Schuck, S.: Identifying profiles and symp- toms of patients with long COVID in France: Data mining infodemiology study ba sed on social me...

    2022 doi

  83. [91]

    PLoS One

    Segneri, L., Babina, N., Hammerschmidt, T., Fronzetti Colladon, A., Gloor, P.A.: Too much focus on your health might be bad for your health: Reddit user’s communication style predicts their Long COVID likelihood. PLoS One. 19, e0308340 (2024). https://doi.org/10.1371/journal.p...

    2024 doi

  84. [93]

    Mining Long -COVID symptoms from Reddit: what we know so far, https://www.re- searchgate.net/profile/Abeed-Sarker/publication/352208391_Mining_Long- COVID_symptoms_from_Reddit_what_we_know_so_far/links/60bedc6ca6fdcc22eae8b 87a/Mining-Long-COVID-symptoms-from-Reddit-what-we-kn...

    2024

  85. [96]

    Jordan, A.A.D.: Understanding the plight of covid-19 long haulers through computational analysis of YouTube content, (2022)

    2022

  86. [97]

    In: Nagar et al, A

    Minel, B.J.A.: Using topic modeling and NLP tools for analyzing long Covid coverage by French press and Twitter. In: Nagar et al, A. (ed.) Intelligent Sustainable Systems, Lecture Notes in Networks and Systems 817. Springer Nature Singapore, Singapore (202 4). https://doi.org/...

    doi

  87. [98]

    Ozduran, E., Büyükçoban, S.: A content analysis of the reliability and quality of Youtube videos as a source of information on health -related post-COVID pain. PeerJ. 10, e14089 (2022). https://doi.org/10.7717/peerj.14089

    2022 doi

  88. [99]

    https://doi.org/10.31219/osf.io/f48yg

    Déom, N., Khalid, A.F., Martin, S., Chepo, M., Vindrola-Padros, C.: Unlocking the mys- teries of long COVID in children and young people: Insights from a policy review and social media analysis in the UK, https://osf.io/preprints/f48yg/, (2023). https://doi.org/10.31219/osf.io/f48yg

    2023 doi

  89. [100]

    Jacques, E.T., Basch, C.H., Park, E., Kollia, B., Barry, E.: Long haul COVID -19 videos on YouTube: Implications for health communication. J. Community Health. 47, 610 –615 (2022). https://doi.org/10.1007/s10900-022-01086-4

    2022 doi

  90. [101]

    Vaccines (Basel)

    Strain, W.D., Sherwood, O., Banerjee, A., Van der Togt, V., Hishmeh, L., Rossman, J.: The impact of COVID vaccination on symptoms of long COVID: An international survey 27 of people with lived experience of long COVID. Vaccines (Basel). 10, 652 (2022). https://doi.org/10.3390/...

    2022 doi

  91. [102]

    Wongtavavimarn, K.: Social support and narrative sensemaking online: A content analysis of Facebook posts by COVID -19 long haulers, https://uh -ir.tdl.org/bitstream/han- dle/10657/10745/WONGTAVAVIMARN-THESIS-2022.pdf?sequence=1, last accessed 2024/12/24

    2022

  92. [103]

    Helmy, Y.A., Fawzy, M., Elaswad, A., Sobieh, A., Kenney, S.P., Shehata, A.A.: The COVID-19 pandemic: A comprehensive review of taxonomy, genetics, epidemiology, di- agnosis, treatment, and control. J. Clin. Med. 9, 1225 (2020). https://doi.org/10.3390/jcm9041225

    2020 doi

  93. [104]

    Lancet Digit

    Gasser, U., Ienca, M., Scheibner, J., Sleigh, J., Vayena, E.: Digital tools against COVID - 19: taxonomy, ethical challenges, and navigation aid. Lancet Digit. Health. 2, e425 –e434 (2020). https://doi.org/10.1016/s2589-7500(20)30137-0

    2020 doi

  94. [105]

    Albahri, O.S., Zaidan, A.A., Albahri, A.S., Zaidan, B.B., Abdulkareem, K.H., Al -qaysi, Z.T., Alamoodi, A.H., Aleesa, A.M., Chyad, M.A., Alesa, R.M., Lim, C.K., Lakulu, M.M., Ibrahim, A.B., Rashid, N.A.: Systematic review of artificial intelligence techniques in the detection ...

    2020

  95. [106]

    Sensors (Basel)

    Awassa, L., Jdey, I., Dhahri, H., Hcini, G., Mahmood, A., Othman, E., Haneef, M.: Study of different deep learning methods for Coronavirus (COVID -19) pandemic: Taxonomy, survey and insights. Sensors (Basel). 22, 1890 (2022). https://doi.org/10.3390/s22051890

    2022 doi

  96. [107]

    Shahid, Z., Kalayanamitra, R., McClafferty, B., Kepko, D., Ramgobin, D., Patel, R., Ag- garwal, C.S., Vunnam, R., Sahu, N., Bhatt, D., Jones, K., Golamari, R., Jain, R.: COVID‐ 19 and older adults: What we know. J. Am. Geriatr. Soc. 68, 926 –929 (2020). https://doi.org/10.1111...

    2020 doi

  97. [108]

    Thakur, N., Han, C.Y.: A study of fall detection in assisted living: Identifying and improv- ing the optimal machine learning method. J. Sens. Actuator Netw. 10, 39 (2021). https://doi.org/10.3390/jsan10030039

    2021 doi

  98. [109]

    JMIR Aging

    Lebrasseur, A., Fortin-Bédard, N., Lettre, J., Raymond, E., Bussières, E.-L., Lapierre, N., Faieta, J., Vincent, C., Duchesne, L., Ouellet, M.-C., Gagnon, E., Tourigny, A., Lamonta- gne, M.-È., Routhier, F.: Impact of the COVID -19 pandemic on older adults: R apid re- view. JM...

    2021 doi

  99. [110]

    Information (Basel)

    Thakur, N., Han, C.Y.: Multimodal approaches for Indoor Localization for Ambient As- sisted Living in Smart Homes. Information (Basel). 12, 114 (2021). https://doi.org/10.3390/info12030114

    2021 doi

  100. [111]

    Aging Clin

    Nanda, A., Vura, N.V.R.K., Gravenstein, S.: COVID-19 in older adults. Aging Clin. Exp. Res. 32, 1199–1202 (2020). https://doi.org/10.1007/s40520-020-01581-5

    2020 doi

  101. [112]

    Miller, E.A.: Protecting and improving the lives of older adults in the COVID -19 era. J. Aging Soc. Policy. 32, 297–309 (2020). https://doi.org/10.1080/08959420.2020.1780104

    2020

  102. [113]

    Big Data Cogn

    Thakur, N., Han, C.Y.: Indoor localization for personalized ambient assisted living of mul- tiple users in multi -floor smart environments. Big Data Cogn. Comput. 5, 42 (2021). https://doi.org/10.3390/bdcc5030042

    2021 doi

  103. [114]

    Perkins, PhD, RNLD, FAAIDD, FGSA: Physical Change and aging, Seventh Edition: A guide for Helping Pro- fessions

    Saxon, S.V., Mary Jean Etten, EdD, GNP, CMP, FT, Elizabeth A. Perkins, PhD, RNLD, FAAIDD, FGSA: Physical Change and aging, Seventh Edition: A guide for Helping Pro- fessions. Springer Publishing Company (2021)

    2021

  104. [115]

    Demography

    Engelman, M., Jackson, H.: Gradual change, homeostasis, and punctuated equilibrium: Reconsidering patterns of health in later life. Demography. 56, 2323 –2347 (2019). https://doi.org/10.1007/s13524-019-00826-x. 28

    2019 doi

  105. [116]

    In: Advances in Intelligent Systems and Computing

    Thakur, N., Han, C.Y.: An intelligent ubiquitous activity aware framework for smart home. In: Advances in Intelligent Systems and Computing. pp. 296 –302. Springer International Publishing, Cham (2021)

    2021

  106. [117]

    Busse, P.J., Mathur, S.K.: Age -related changes in immune function: Effect on airway in- flammation. J. Allergy Clin. Immunol. 126, 690 –699 (2010). https://doi.org/10.1016/j.jaci.2010.08.011

    2010 doi

  107. [118]

    Aging (Albany NY)

    Howcroft, T.K., Campisi, J., Louis, G.B., Smith, M.T., Wise, B., Wyss-Coray, T., Augus- tine, A.D., McElhaney, J.E., Kohanski, R., Sierra, F.: The role of inflammation in age - related disease. Aging (Albany NY). 5, 84 –93 (2013). https://doi.org/10.18632/ag- ing.100531

    2013 doi

  108. [119]

    Pawelec, G., Goldeck, D., Derhovanessian, E.: Inflammation, ageing and chronic disease. Curr. Opin. Immunol. 29, 23–28 (2014). https://doi.org/10.1016/j.coi.2014.03.007

    2014 doi

  109. [120]

    Springer Nature Switzerland, Cham (2024)

    2024

  110. [121]

    In: Advances in Intelligent Systems and Computing

    Thakur, N., Han, C.Y.: A framework for facilitating human-human interactions to mitigate loneliness in elderly. In: Advances in Intelligent Systems and Computing. pp. 322 –327. Springer International Publishing, Cham (2021)

    2021

  111. [122]

    Routledge, Fourth edition

    Novak, M.: Issues in aging. Routledge, Fourth edition. | New York, NY : Routledge, 2018. (2018)

    2018

  112. [123]

    Silverstein, M.: Meeting the challenges of an aging workforce. Am. J. Ind. Med. 51, 269– 280 (2008). https://doi.org/10.1002/ajim.20569

    2008 doi

  113. [124]

    Han, C.: Pervasive activity logging for indoor localization in smart homes

    Thakur, N., Y. Han, C.: Pervasive activity logging for indoor localization in smart homes. In: 2021 4th International Conference on Data Science and Information Technology. pp. 246–255. ACM, New York, NY, USA (2021)

    2021

  114. [125]

    In: Lecture Notes in Computer Science

    Thakur, N., Han, C.Y.: An improved approach for complex activity recognition in smart homes. In: Lecture Notes in Computer Science. pp. 220–231. Springer International Pub- lishing, Cham (2019)

    2019

  115. [126]

    In: 2018 IEEE 5G World Forum (5GWF)

    Thakur, N., Han, C.Y.: An activity analysis model for enhancing user experiences in affect aware systems. In: 2018 IEEE 5G World Forum (5GWF). pp. 516–519. IEEE (2018)

    2018

  116. [127]

    In: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)

    Thakur, N., Han, C.Y.: A context-driven complex activity framework for smart home. In: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). pp. 801–806. IEEE (2018)

    2018

  117. [128]

    Kogan, A.C., Wilber, K., Mosqueda, L.: Person‐centered care for older adults with chronic conditions and functional impairment: A systematic literature review. J. Am. Geriatr. Soc. 64, (2016). https://doi.org/10.1111/jgs.13873

    2016 doi

  118. [129]

    JMIR MHealth UHealth

    Matthew-Maich, N., Harris, L., Ploeg, J., Markle-Reid, M., Valaitis, R., Ibrahim, S., Gafni, A., Isaacs, S.: Designing, implementing, and evaluating mobile health technologies for managing chronic conditions in older adults: A scoping review. JMIR MHealth UHealth. 4, e29 (2016...

    2016 doi

  119. [130]

    PACC Research Group, Anaby, D., Miller, W.C., Eng, J.J., Jarus, T., Noreau, L.: Partici- pation and well-being among older adults living with chronic conditions. Soc. Indic. Res. 100, 171–183 (2011). https://doi.org/10.1007/s11205-010-9611-x

    2011 doi

  120. [131]

    Ferrari, M., Harrison, B., Rawashdeh, O., Hammond, R., Avery, Y., Rawashdeh, M., Sa’deh, W., Maddens, M.: Clinical feasibility trial of a motion detection system for fall prevention in hospitalized older adult patients. Geriatr. Nurs. 33, 177 –183 (2012). https://doi.org/10.10...

    2012 doi

  121. [132]

    Information (Basel)

    Thakur, N., Han, C.Y.: A simplistic and cost-effective design for real-world development of an ambient assisted living system for fall detection and indoor localization: Proof -of- concept. Information (Basel). 13, 363 (2022). https://doi.org/10.3390/info13080363

    2022 doi

  122. [133]

    BMC Cancer

    Townsley, C.A., Chan, K.K., Pond, G.R., Marquez, C., Siu, L.L., Straus, S.E.: Understand- ing the attitudes of the elderly towards enrolment into cancer clinical trials. BMC Cancer. 6, (2006). https://doi.org/10.1186/1471-2407-6-34. 29

    2006 doi

  123. [134]

    In: 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE)

    Thakur, N., Han, C.Y.: A framework for prediction of cramps during activities of daily living in elderly. In: 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). pp. 284–287. IEEE (2020)

    2020

  124. [135]

    Fontes, A.P., Neri, A.L.: Resilience in aging: literature review. Cien. Saude Colet. 20, 1475–1495 (2015). https://doi.org/10.1590/1413-81232015205.00502014

    2015

  125. [136]

    In: Lecture Notes in Networks and Systems

    Thakur, N., Han, C.Y.: A multimodal approach for early detection of cognitive impairment from tweets. In: Lecture Notes in Networks and Systems. pp. 11–19. Springer International Publishing, Cham (2022)

    2022

  126. [137]

    Neurobiol

    Stern, Y., Albert, M., Barnes, C.A., Cabeza, R., Pascual -Leone, A., Rapp, P.R.: A frame- work for concepts of reserve and resilience in aging. Neurobiol. Aging. 124, 100 –103 (2023). https://doi.org/10.1016/j.neurobiolaging.2022.10.015

    2023 doi

  127. [138]

    Ageing Res

    Chen, Z., Yu, J., Song, Y., Chui, D.: Aging Beijing: Challenges and strategies of health care for the elderly. Ageing Res. Rev. 9, S2 –S5 (2010). https://doi.org/10.1016/j.arr.2010.07.001

    2010 doi

  128. [139]

    In: Advances in Intelligent Systems and Computing

    Thakur, N., Han, C.Y.: Towards a knowledge base for activity recognition of diverse users. In: Advances in Intelligent Systems and Computing. pp. 303 –308. Springer International Publishing, Cham (2021)

    2021

  129. [140]

    Dunér, A., Nordström, M.: Intentions and strategies among elderly people: Coping in eve- ryday life. J. Aging Stud. 19, 437 –451 (2005). https://doi.org/10.1016/j.jag- ing.2004.10.001

    2005 doi

  130. [141]

    BMC Geriatr

    Spoorenberg, S.L.W., Uittenbroek, R.J., Middel, B., Kremer, B.P.H., Reijneveld, S.A., Wynia, K.: Embrace, a model for integrated elderly care: study protocol of a randomized controlled trial on the effectiveness regarding patient outcomes, service use, cos ts, and quality of c...

    2013 doi

  131. [143]

    New Media Soc

    Bainotti, L., Caliandro, A., Gandini, A.: From archive cultures to ephemeral content, and back: Studying Instagram Stories with digital methods. New Media Soc. 23, 3656 –3676 (2021). https://doi.org/10.1177/1461444820960071

    2021 doi

  132. [144]

    Cardell, K., Douglas, K., Maguire, E.: ‘stories.’ In: Mediating Memory. pp. 157 –172. Routledge (2017)

    2017

  133. [145]

    Lin, H.: ‘Let’s purchase coloured live chat messages’: the impact of user engagement with Super Chat on YouTube. Inf. Commun. Soc. 1 –19 (2024). https://doi.org/10.1080/1369118x.2024.2442407

    2024

  134. [146]

    Sun, H., Chen, J., Fan, M.: Effect of live chat on traffic‐to‐sales conversion: Evidence from an online marketplace. Prod. Oper. Manag. 30, 1201 –1219 (2021). https://doi.org/10.1111/poms.13320

    2021 doi

  135. [147]

    Proceedings of the International AAAI Conference on Web and Social Media

    Perez, B., Musolesi, M., Stringhini, G.: You are your metadata: Identification and obfus- cation of social media users using metadata information. Proceedings of the International AAAI Conference on Web and Social Media. 12, (2018). https://doi.org/10.1609/i c- wsm.v12i1.15010

    2018 doi

  136. [148]

    -S., Lin, Z

    Chen, L. -S., Lin, Z. -C., Chang, J. -R.: FIR: An effective scheme for extracting useful metadata from social media. J. Med. Syst. 39, (2015). https://doi.org/10.1007/s10916-015- 0333-0

    2015 doi

  137. [149]

    TechTrends

    Gerber, H.R., Lynch, T.L.: Into the meta: Research methods for moving beyond social media surfacing. TechTrends. 61, 263 –272 (2017). https://doi.org/10.1007/s11528 -016- 0140-6. 30

    2017 doi

  138. [150]

    In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

    Jones, S.M., Neblitt -Jones, V., Weigle, M.C., Klein, M., Nelson, M.L.: It’s all about the cards: Sharing on social media encouraged HTML metadata growth. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL). pp. 110–119. IEEE (2021)

    2021

  139. [151]

    Rolls, K., Hansen, M., Jackson, D., Elliott, D.: How health care professionals use social media to create virtual communities: An integrative review. J. Med. Internet Res. 18, e166 (2016). https://doi.org/10.2196/jmir.5312

    2016 doi

  140. [152]

    Lu, Y., Wu, Y., Liu, J., Li, J., Zhang, P.: Understanding health care social media use from different stakeholder perspectives: A content analysis of an online health community. J. Med. Internet Res. 19, e109 (2017). https://doi.org/10.2196/jmir.7087

    2017 doi

  141. [153]

    IEEE Access

    Sundaram, A., Subramaniam, H., Hamid, S.H.A., Nor, A.M.: A systematic literature re- view on social media slang analytics in contemporary discourse. IEEE Access. 11, 132457–132471 (2023). https://doi.org/10.1109/access.2023.3334278

    2023

  142. [154]

    In: 2024 International Confer- ence on Trends in Quantum Computing and Emerging Business Technologies

    Sundaram, A., Subramaniam, H., Ab Hamid, S.H., Nor, A.M.: A three -step procedural paradigm for domain-specific social media slang analytics. In: 2024 International Confer- ence on Trends in Quantum Computing and Emerging Business Technologies. pp. 1 –7. IEEE (2024)

    2024

  143. [155]

    CAAI Trans

    Matsumoto, K., Ren, F., Matsuoka, M., Yoshida, M., Kita, K.: Slang feature extraction by analysing topic change on social media. CAAI Trans. Intell. Technol. 4, 64 –71 (2019). https://doi.org/10.1049/trit.2018.1060

    2019

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.