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Nir Lotan

dblp:305/7971 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Web and social media mining · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computational social science and digital humanities · 100%
Artificial intelligence
1 paper
Information extraction and text analysis · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational social science and digital humanities › political science
online political discourse
0.812024
A Closer Look at Multidimensional Online Political Incivility · EMNLP 2024
Web and social media mining
social media analysis
0.812024
A Closer Look at Multidimensional Online Political Incivility · EMNLP 2024
Web and social media mining › social media analysis
twitter analysis
0.812024
A Closer Look at Multidimensional Online Political Incivility · EMNLP 2024
Natural language and speech › Information extraction and text analysis
text classification
0.212024
A Closer Look at Multidimensional Online Political Incivility · EMNLP 2024

Methods — techniques the papers use, named apart from their topics

text classifier · 2.3crowdsourcing · 2.3
YearPublicationVenuePosition
2024 A Closer Look at Multidimensional Online Political Incivility
abstract
Toxic online political discourse has become prevalent, where scholars debate about its impact on democratic processes.This work presents a large-scale study of political incivility on Twitter.In line with theories of political communication, we differentiate between harsh impolite style and intolerant substance.We present a dataset of 13K political tweets in the U.S. context, which we collected and labeled by this multidimensional distinction using crowd sourcing.The evaluation of state-of-the-art classifiers illustrates the challenges involved in political incivility detection, which often requires high-level semantic and social understanding.Nevertheless, performing incivility detection at scale, we are able to characterise its distribution across individual users and geopolitical regions.Our findings align with and extend existing theories of political communication.In particular, we find that roughly 80% of the uncivil tweets are authored by 20% of the users, where users who are politically engaged are more inclined to use uncivil language.We further find that political incivility exhibits network homophily, and that incivility is more prominent in highly competitive geopolitical regions.Our results apply to both uncivil style and substance.Warning: The paper contains examples that readers might find offensive.
Sagi Pendzel, Nir Lotan, Alon Zoizner, Einat Minkov
EMNLP2