Kailai Yang

19 papers receiving 451 citations

Kailai Yang's Hit Papers

MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models 2024 · 57 citations
570+1Years since publication1020304050

Peers

Kailai Yang
Comparison fields: 5 of 71
  • Applied Psychology 95
  • Health Informatics 15
  • Experimental and Cognitive Psychology 114
  • Social Psychology 179
  • Artificial Intelligence 218
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Ermal Toto United States
Chujie Zheng China
Sungwoo Lee South Korea
Tulika Saha India
Alex Vakaloudis Ireland
Larry Chan United States
Elvis Saravia Taiwan
Ethel Ong Philippines
Natalie Parde United States
Swee Lan See Singapore
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Citations per field
00.5×6.3×
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Citations per year

Countries citing papers authored by Kailai Yang

Since Specialization
Citations

This map shows the geographic impact of Kailai Yang'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 Kailai Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kailai Yang more than expected).

Fields of papers citing papers by Kailai Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kailai Yang. 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 Kailai Yang. The network helps show where Kailai Yang may publish in the future.

Co-authors

The 25 scholars most cited alongside Kailai Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kailai Yang Line = papers co-authored together Kailai Yang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202365
2 202262
3
MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models
Hit paper breakdown →
202457
4 202243
5 202343
6 202234
7 202429
8 202125
9 202318
10 202418
11 202417
12 202013
13 20239
14 20246
15 20255
16 20234
17 20243
18 20233
19 20251
20 20250

About Kailai Yang

Kailai Yang is a scholar working on Artificial Intelligence, Social Psychology, Sociology and Political Science, Information Systems and Experimental and Cognitive Psychology, having authored 20 papers that have together received 455 indexed citations. Recurring topics across this work include Mental Health via Writing (11 papers), Sentiment Analysis and Opinion Mining (11 papers), Spam and Phishing Detection (4 papers), Misinformation and Its Impacts (4 papers), Digital Mental Health Interventions (4 papers), Advanced Text Analysis Techniques (3 papers), Mental Health Research Topics (2 papers) and Hate Speech and Cyberbullying Detection (2 papers). The work is most often cited by research in Applied Psychology (95 citations), Health Informatics (15 citations), Experimental and Cognitive Psychology (114 citations), Social Psychology (179 citations) and Artificial Intelligence (218 citations). Kailai Yang has collaborated with scholars based in United Kingdom, China and Finland. Frequent co-authors include Sophia Ananiadou, Tianlin Zhang, Qianqian Xie, Shaoxiong Ji, Jimin Huang, Yucheng Zou, Hui Zhang, Zeyu Qin, Yipeng Zhang and Chengjie Sun. Their work appears in journals such as Information Processing & Management, Information Fusion, IEEE Transactions on Affective Computing, Current Treatment Options in Psychiatry and Food Packaging and Shelf Life.

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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