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Sara Porat

dblp:04/1282 · DBLP profile ↗
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9ranked-venue papers
7as first author
0since 2021 · last 2014
—ORCID · none

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

Theory of computation · 6 · 6 first-authorArtificial intelligence and machine learning · 2 · 2 first-authorSoftware engineering, systems software and programming languages · 1Human-computer interaction and ubiquitous computing · 1

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.

Human-computer interaction and pervasive computing
1 paper
Collaborative and social computing · 100%
Theoretical computer science
2 papers
Automata and formal languages · 82% Logic in computer science · 18%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Collaborative and social computing › social media
enterprise social media
0.212014
The perception of others: inferring reputation from social media in the enterprise · CSCW 2014
Collaborative and social computing
social media
0.212014
The perception of others: inferring reputation from social media in the enterprise · CSCW 2014
Collaborative and social computing
social computing
0.112014
The perception of others: inferring reputation from social media in the enterprise · CSCW 2014
Automata and formal languages › formal grammars
context-free grammar
0.021989
Fairness in Context-Free Grammars under Every Choice-strategy · Inf. Comput. 1989
Fair Deriviations in Context-Free Grammars · Inf. Control. 1982

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

user study · 0.2trace analysis · 0.2
YearPublicationVenuePosition
2014 The perception of others: inferring reputation from social media in the enterprise
abstract
The emergence of social media allows people to interact with others all over the world. During interaction, people leave many traces behind that can reveal things about themselves, or about how they perceive others: having many followers may indicate that one is an influencer; forum answers that gain high ranking, are likely to testify for expertise; people who gain high ranking in eCommerce sites are likely to be trustworthy. In this paper, we examine whether public online traces can be used for inferring the reputation of a person as perceived by others in relation to trustworthiness, influence, expertise, and impact. We describe a study performed on indicators of reputation that employees leave in a rich organizational social media platform. We compare different indicators, and report the results of an extensive user study with over 500 participants who provided their perception of thousands of others through a set of hypothetical scenarios.
Michal Jacovi, Ido Guy, Shiri Kremer-Davidson, Sara Porat, Neta Aizenbud-Reshef
CSCW4
2001 Sealing, Encapsulation, and Mutability
Marina Biberstein, Joseph Gil, Sara Porat
ECOOP3
1991 Learning Automata from Ordered Examples
Sara Porat, Jerome A. Feldman
Mach. Learn.1
1989 Fairness in Context-Free Grammars under Every Choice-strategy
Sara Porat, Nissim Francez
Inf. Comput.1
1986 Full-Commutation and Fair-Termination in Equational (and Combined) Term-Rewriting Systems
Sara Porat, Nissim Francez
CADE1
1985 Fairness in Term Rewriting Systems
Sara Porat, Nissim Francez
RTA1
1985 Fairness in Context-Free Grammars under Canonical Derivations
Sara Porat, Nissim Francez
STACS1
1984 Towards a Hierarchy of Nets
Sara Porat, Michael Yoeli
J. Comput. Syst. Sci.1
1982 Fair Deriviations in Context-Free Grammars
Sara Porat, Nissim Francez, Shlomo Moran, Shmuel Zaks
Inf. Control.1