Peer to Peer Lending: The Relationship Between Language Features, Trustworthiness, and Persuasion Success

By Laura Larrimore, Li Jiang, Jeff Larrimore, David M. Markowitz, S. Gorski. 2011

295 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Social Psychology

View Paper: https://www.semanticscholar.org/paper/5c388f0e65592f1655cc4698f8066579bf22af7d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. David M. Markowitz
2. Jeffrey T. Hancock
3. Hui Xiong
4. Chuanren Liu
5. Catalina L. Toma

In Information Systems

1. Jeffrey T. Hancock
2. Hui Xiong
3. David M. Markowitz

In Social Psychology

1. Jeffrey T. Hancock
2. David M. Markowitz
3. Catalina L. Toma

Citations in the corpus

• Evaluating credit risk and loan performance in online Peer-to-Peer (P2P) lending (2015, background): “Larrimore et al. (2011) demonstrate that borrowers who use extended narratives, concrete descriptions and quantitative words have positive impact on funding success.”
• Linguistic Obfuscation in Fraudulent Science (2016, methodology): “An abstraction index was constructed by taking the inverse of the sum of the standardized LIWC scores for articles, prepositions, and quantifiers (Larrimore et al., 2011).”
• Instance-based credit risk assessment for investment decisions in P2P lending (2016, background): “For instance, Larrimore et al. (2011) analyzed the impact of language used in the listing, which can help borrowers strengthen persuasiveness for getting funds.”
• What Lies Beneath: The Linguistic Traces of Deception in Online Dating Profiles (2012, background): “Consistent with this prediction, requests for loans on a peer-to-peer lending site that used more concrete language received more money from lenders (Larrimore et al., 2011), suggesting that lenders placed more trust in more concretely worded loan requests.”