Dan Goldwasser
- Artificial Intelligence top 1%
- Topic Modeling 45
- Natural Language Processing Techniques 27
- Sentiment Analysis and Opinion Mining 22
- Hate Speech and Cyberbullying Detection 9
- Advanced Text Analysis Techniques 9
- General Social Sciences top 0.5%
- Communication top 5%
- Social Media and Politics 13
- Software top 10%
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- Misinformation and Its Impacts 11
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- Complex Network Analysis Techniques 8
Dan Goldwasser
83 papers receiving 1.4k citations
Peers
Comparison fields: 5 of 89
- Computer Science Applications 213
- Artificial Intelligence 1.1k
- General Social Sciences 85
- Communication 117
- Software 45
Countries citing papers authored by Dan Goldwasser
This map shows the geographic impact of Dan Goldwasser'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 Dan Goldwasser with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Goldwasser more than expected).
Fields of papers citing papers by Dan Goldwasser
This network shows the impact of papers produced by Dan Goldwasser. 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 Dan Goldwasser. The network helps show where Dan Goldwasser may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Dan Goldwasser, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 0 | |
| 4 | 2023 | 3 | |
| 5 | 2023 | 4 | |
| 6 | 2020 | 30 | |
| 7 | Understanding Learners' Opinion about Participation Certificates in Online Courses Using Topic Modeling. | 2018 | 5 |
| 8 | 2018 | 45 | |
| 9 | Structured Representation Learning for Online Debate Stance Prediction | 2018 | 12 |
| 10 | “All I know about politics is what I read in Twitter”: Weakly Supervised Models for Extracting Politicians’ Stances From Twitter | 2016 | 18 |
| 11 | SNIPE: signature generation for phishing emails | 2015 | 1 |
| 12 | 2014 | 65 | |
| 13 | Leveraging Domain-Independent Information in Semantic Parsing | 2013 | 4 |
| 14 | Predicting Structures in NLP: Constrained Conditional Models and Integer Linear Programming in NLP | 2012 | 3 |
| 15 | Confidence Driven Unsupervised Semantic Parsing | 2011 | 49 |
| 16 | Driving Semantic Parsing from the World's Response | 2010 | 138 |
| 17 | Structured Output Learning with Indirect Supervision | 2010 | 35 |
| 18 | Discriminative Learning over Constrained Latent Representations | 2010 | 44 |
| 19 | Relation Alignment for Textual Entailment Recognition. | 2009 | 21 |
| 20 | Identifying Inter-Domain Similarities through Content-Based Analysis of Hierarchical Web-Directories | 2006 | 3 |
About Dan Goldwasser
Dan Goldwasser is a scholar working on Communication, Artificial Intelligence and General Social Sciences, having authored 88 papers that have together received 1.5k indexed citations. Recurring topics across this work include Topic Modeling (45 papers), Natural Language Processing Techniques (27 papers), Sentiment Analysis and Opinion Mining (22 papers), Social Media and Politics (13 papers), Misinformation and Its Impacts (11 papers), Hate Speech and Cyberbullying Detection (9 papers), Advanced Text Analysis Techniques (9 papers) and Complex Network Analysis Techniques (8 papers). The work is most often cited by research in Computer Science Applications (213 citations), Artificial Intelligence (1.1k citations) and General Social Sciences (85 citations). Dan Goldwasser has collaborated with scholars based in United States, Israel and Italy. Frequent co-authors include Dan Roth, Kristen Johnson, Ming‐Wei Chang, Hal Daumé, James Clarke, Chang Li, Bert Huang, Arti Ramesh, Lise Getoor and I‐Te Lee. Their work appears in journals such as Machine Learning, IEEE Transactions on Visualization and Computer Graphics and IEEE Transactions on Dependable and Secure Computing.
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.