Parth Patwa

486 total citations
12 papers, 150 citations indexed

About

Parth Patwa is a scholar working on Artificial Intelligence, Sociology and Political Science and Human-Computer Interaction. According to data from OpenAlex, Parth Patwa has authored 12 papers receiving a total of 150 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 3 papers in Sociology and Political Science and 3 papers in Human-Computer Interaction. Recurrent topics in Parth Patwa's work include Sentiment Analysis and Opinion Mining (4 papers), Hate Speech and Cyberbullying Detection (4 papers) and Topic Modeling (2 papers). Parth Patwa is often cited by papers focused on Sentiment Analysis and Opinion Mining (4 papers), Hate Speech and Cyberbullying Detection (4 papers) and Topic Modeling (2 papers). Parth Patwa collaborates with scholars based in India, United States and Norway. Parth Patwa's co-authors include Amitava Das, Srinivas Pykl, Thamar Solorio, Björn Gambäck, Tanmoy Chakraborty, Sudipta Kar, Gustavo Aguilar, Prerana Mukherjee, Achuta Kadambi and Laleh Jalilian and has published in prestigious journals such as AI Magazine, SN Computer Science and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Parth Patwa

8 papers receiving 136 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Parth Patwa India	5	116	22	19	18	16	12	150
Wissam Antoun Lebanon	5	175 1.5×	10 0.5×	59 3.1×	37 2.1×	3 0.2×	6	221
Heliodoro Tejeda United States	6	78 0.7×	7 0.3×	9 0.5×	7 0.4×	29 1.8×	6	190
Mohamed R. Shoaib Egypt	9	56 0.5×	14 0.6×	13 0.7×	9 0.5×	7 0.4×	20	198
Katherine A. Keith United States	5	101 0.9×	9 0.4×	14 0.7×	11 0.6×	1 0.1×	9	250
Mohammad Nur Nobi United States	2	69 0.6×	7 0.3×	19 1.0×	23 1.3×	2 0.1×	3	219
Alejandro Guerra‐Hernández Mexico	6	52 0.4×	12 0.5×	24 1.3×	4 0.2×	4 0.3×	22	145
Floris Goes Netherlands	7	25 0.2×	46 2.1×	4 0.2×	11 0.6×	6 0.4×	8	283
Giulia Orrù Italy	7	34 0.3×	10 0.5×	21 1.1×	4 0.2×	5 0.3×	28	168
Sergey Demyanov Australia	5	92 0.8×	10 0.5×	8 0.4×	6 0.3×		6	143
Joanna Bitton Canada	5	74 0.6×	6 0.3×	3 0.2×	7 0.4×	3 0.2×	6	204

Countries citing papers authored by Parth Patwa

Since Specialization

Citations

This map shows the geographic impact of Parth Patwa's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Parth Patwa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Parth Patwa more than expected).

Fields of papers citing papers by Parth Patwa

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Parth Patwa. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Parth Patwa. The network helps show where Parth Patwa may publish in the future.

Co-authorship network of co-authors of Parth Patwa

This figure shows the co-authorship network connecting the top 25 collaborators of Parth Patwa. A scholar is included among the top collaborators of Parth Patwa based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Parth Patwa. Parth Patwa is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

1.

Patwa, Parth, et al.. (2024). Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs. 91–98. 7 indexed citations

2.

Patwa, Parth, et al.. (2022). Synthetic Generation of Face Videos with Plethysmograph Physiology. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 20555–20564. 24 indexed citations

3.

Sahu, Pranjal, et al.. (2022). Offence Detection in Dravidian Languages Using Code-Mixing Index-Based Focal Loss. SN Computer Science. 3(5). 3 indexed citations

4.

Patwa, Parth, et al.. (2022). Memotion Analysis through the Lens of Joint Embedding (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence. 36(11). 12959–12960.

5.

Ali, Mohsin, et al.. (2022). PESTO: Switching Point Based Dynamic and Relative Positional Encoding for Code-Mixed Languages (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence. 36(11). 12901–12902.

6.

Patwa, Parth, et al.. (2022). Large Scale Multilingual Sticker Recommendation In Messaging Apps. AI Magazine. 42(4). 16–28.

7.

Patwa, Parth, et al.. (2021). Large scale multilingual sticker recommendation in messaging apps. AI Magazine. 42(4). 16–28.

8.

Patwa, Parth, et al.. (2021). COVID-19 Outbreak Prediction and Analysis using Self Reported Symptoms. 154–169. 1 indexed citations

9.

Doddapaneni, Sumanth, et al.. (2021). Bitions@DravidianLangTech-EACL2021: Ensemble of Multilingual Language Models with Pseudo Labeling for offence Detection in Dravidian Languages. 291–299. 1 indexed citations

10.

Patwa, Parth, Gustavo Aguilar, Sudipta Kar, et al.. (2020). SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets. 774–790. 69 indexed citations

11.

Patwa, Parth, et al.. (2020). Aggression and Misogyny Detection using BERT: A Multi-Task Approach. 126–131. 38 indexed citations

12.

Patwa, Parth, et al.. (2020). Understanding Chat Messages for Sticker Recommendation in Messaging Apps. Proceedings of the AAAI Conference on Artificial Intelligence. 34(8). 13156–13163. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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