---
title: "baidu-aip (Python package): papers citing it"
description: "3 papers in the tracked journals link to baidu-aip (Python package) in their Crossref records, the first in 2020; most of them (1) in 2023."
url: https://scholar4ai.com/code/pypi-baidu-aip
markdown_url: https://scholar4ai.com/code/pypi-baidu-aip.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "3 papers in the tracked journals link to baidu-aip (Python package) in their Crossref records, the first in 2020; most of them (1) in 2023. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# baidu-aip (Python package): papers linking to it

Research papers in the 480 tracked journals that link to baidu-aip (Python package) (https://pypi.org/project/baidu-aip/), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- PyPI
- https://pypi.org/project/baidu-aip/

All repositories: https://scholar4ai.com/code.md

## Key findings

- 3 papers in the tracked journals link to baidu-aip (Python package) in their Crossref records, the first in 2020; most of them (1) in 2023.
- By category: AI (3); the journals with the most are Information Processing & Management (3).
- 3 of the papers build on one another: a later paper cites an earlier one that also links baidu-aip (Python package).

## By year

*Papers linking to baidu-aip (Python package) by publication year*

| Year | Papers |
|---|---:|
| 2020 | 1 |
| 2022 | 1 |
| 2023 | 1 |

## Journals

*Journals whose papers link to baidu-aip (Python package) most*

| Journal | Papers |
|---|---:|
| [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | 3 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Social media rumor refutation effectiveness: Evaluation, modelling and enhancement](https://doi.org/10.1016/j.ipm.2020.102420) (Information Processing & Management, 2020)
  - [Lifecycle research of social media rumor refutation effectiveness based on machine learning and visualization technology](https://doi.org/10.1016/j.ipm.2022.103077) (Information Processing & Management, 2022) — cites the paper above
    - [Configurational patterns for COVID-19 related social media rumor refutation effectiveness enhancement based on machine learning and fsQCA](https://doi.org/10.1016/j.ipm.2023.103303) (Information Processing & Management, 2023) — cites the paper above

## Papers

*Papers linking to baidu-aip (Python package), most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2020-10-23 | [Social media rumor refutation effectiveness: Evaluation, modelling and enhancement](https://doi.org/10.1016/j.ipm.2020.102420) | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | Zongmin Li, Qi Zhang, Xinyu Du et al. | 113 | cites it |
| 2023-02-01 | [Configurational patterns for COVID-19 related social media rumor refutation effectiveness enhancement based on machine learning and fsQCA](https://doi.org/10.1016/j.ipm.2023.103303) | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | Zongmin Li, Ye Zhao, Tie Duan et al. | 36 | cites it |
| 2022-09-20 | [Lifecycle research of social media rumor refutation effectiveness based on machine learning and visualization technology](https://doi.org/10.1016/j.ipm.2022.103077) | [Information Processing & Management](https://scholar4ai.com/journals/information-processing-management.md) | Zongmin Li, Xinyu Du, Ye Zhao et al. | 30 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). baidu-aip (Python package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/pypi-baidu-aip

```bibtex
@misc{scholar4ai-code-pypi-baidu-aip,
  author = {{Scholar4AI}},
  title = {baidu-aip (Python package): papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/pypi-baidu-aip}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).