What is Your Organization 'Like'?: A Study of Liking Activity in the
Enterprise
Workplace Social Performance
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Guy, Ido
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Ronen, Inbal
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Zwerdling, Naama
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Zuyev-Grabovitch, Irena
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Jacovi, Michal
Proceedings of the ACM CHI'16 Conference on Human Factors in Computing
Systems
2016-05-07
v.1
p.3025-3037
© Copyright 2016 ACM
Summary: The 'like' button, introduced by Facebook several years ago, has become one
of the most prominent icons of social media. Similarly to other popular social
media features on the web, enterprises have also recently adopted it. In this
paper, we present a first comprehensive study of liking activity in the
enterprise. We studied the logs of an enterprise social media platform within a
large global organization along a period of seven months, in which 393,720
'likes' were performed. In addition, we conducted a survey of 571 users of the
platform's 'like' button. Our evaluation combines quantitative and qualitative
analysis to inspect what employees like, why they use the 'like' button, and to
whom they give their 'likes'.
Social Media-Based Expertise Evidence
Papers
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Yogev, Arnon
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Guy, Ido
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Ronen, Inbal
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Zwerdling, Naama
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Barnea, Maya
Proceedings of the 14th European Conference on Computer-Supported
Cooperative Work
2015-09-19
p.63-82
© Copyright 2015 Springer International Publishing Switzerland
Summary: Social media provides a fertile ground for expertise location. The public
nature of the data supports expertise inference with little privacy
infringement and, in addition, presentation of direct and detailed evidence for
an expert's skillfulness in the queried topic. In this work, we study the use
of social media for expertise evidence. We conducted two user surveys of
enterprise social media users within a large global organization, in which
participants were asked to rate anonymous experts based on artificial and real
evidence originating from different types of social media data. Our results
indicate that the social media data types perceived most convincing as evidence
are not necessarily the ones from which expertise can be inferred most
precisely or effectively. We describe these results in detail and discuss
implications for designers and architects of expertise location systems.
Recommending social media content to community owners
Session 3a: Social media
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Ronen, Inbal
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Guy, Ido
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Kravi, Elad
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Barnea, Maya
Proceedings of the 2014 Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval
2014-07-06
p.243-252
© Copyright 2014 ACM
Summary: Online communities within the enterprise offer their leaders an easy and
accessible way to attract, engage, and influence others. Our research studies
the recommendation of social media content to leaders (owners) of online
communities within the enterprise. We developed a system that suggests to
owners new content from outside the community, which might interest the
community members. As online communities are taking a central role in the
pervasion of social media to the enterprise, sharing such recommendations can
help owners create a more lively and engaging community. We compared seven
different methods for generating recommendations, including content-based,
member-based, and hybridization of the two. For member-based recommendations,
we experimented with three groups: owners, active members, and regular members.
Our evaluation is based on a survey in which 851 community owners rated a total
of 8,218 recommended content items. We analyzed the quality of the different
recommendation methods and examined the effect of different community
characteristics, such as type and size.
What motivates members to contribute to enterprise online communities?
Posters
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Ehrlich, Kate
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Muller, Michael
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Matthews, Tara
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Guy, Ido
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Ronen, Inbal
Companion Proceedings of ACM CSCW 2014 Conference on Computer-Supported
Cooperative Work and Social Computing
2014-02-15
v.2
p.149-152
© Copyright 2014 ACM
Summary: A major challenge for online communities is encouraging members to
participate and contribute content to the community. While prior work has
identified motivators to contribute for internet community members, it is
unknown if these are the same for employees in enterprise communities. A
qualitative study with a group of very active employee members of enterprise
communities we called "informal leaders", revealed that they were motivated by
wanting to help other members but only for those communities which related to
their job. In contrast to prior findings from internet communities, they did
not appear to be motivated by the need to develop their reputation or to
connect with others. These results provide new insights into participation in
enterprise online communities.
Mining expertise and interests from social media
Research papers
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Guy, Ido
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Avraham, Uri
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Carmel, David
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Ur, Sigalit
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Jacovi, Michal
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Ronen, Inbal
Proceedings of the 2013 International Conference on the World Wide Web
2013-05-13
v.1
p.515-526
© Copyright 2013 ACM
Summary: The rising popularity of social media in the enterprise presents new
opportunities for one of the organization's most important needs -- expertise
location. Social media data can be very useful for expertise mining due to the
variety of existing applications, the rich metadata, and the diversity of user
associations with content. In this work, we provide an extensive study that
explores the use of social media to infer expertise within a large global
organization. We examine eight different social media applications by
evaluating the data they produce through a large user survey, with 670
enterprise social media users. We distinguish between two semantics that relate
a user to a topic: expertise in the topic and interest in it and compare these
two semantics across the different social media applications.
Finger on the Pulse: The Value of the Activity Stream in the Enterprise
UX in Work/Educational Contexts
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Guy, Ido
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Steier, Tal
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Barnea, Maya
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Ronen, Inbal
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Daniel, Tal
Proceedings of IFIP INTERACT'13: Human-Computer Interaction-4
2013
v.4
p.411-428
Keywords: Activity streams; collaboration; cscw; enterprise; enterprise search;
real-time search; social analytics; social business; social media; social
search; social software; social streams; web 2.0
© Copyright 2013 IFIP
Summary: The activity stream, which syndicates user activities across social media,
has been gaining popularity on the web. With social media infiltrating the
enterprise and higher portions of the workforce becoming accustomed to
consuming information through activity streams, it also has the potential to
play a key role in shaping the workplace. This work provides a first
comprehensive study of an enterprise activity stream. We analyze different
characteristics of the stream, its usage through a faceted search-based
application, and the way users search it compared to traditional enterprise
search. We also discuss various use cases of the stream, both from an
individual employee's perspective and from an organizational perspective,
exposing the potential value and role of the activity stream in the enterprise
of the future.
Swimming against the Streamz: search and analytics over the enterprise
activity stream
Knowledge management short paper session
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Guy, Ido
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Steier, Tal
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Barnea, Maya
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Ronen, Inbal
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Daniel, Tal
Proceedings of the 2012 ACM Conference on Information and Knowledge
Management
2012-10-29
p.1587-1591
© Copyright 2012 ACM
Summary: Activity streams have become prevalent on the web and are starting to emerge
in enterprises. In this work, we present Streamz, a novel application that uses
a faceted search approach to provide employees with advanced capabilities of
search, navigation, attention management, and other types of analytics on top
of an enterprise activity stream. We provide a detailed description of the
Streamz tool as well as usage analysis based on user interface logs and
interviews of active users.
Impression formation in corporate people tagging
Workplace
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Raban, Daphne R.
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Danan, Avinoam
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Ronen, Inbal
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Guy, Ido
Proceedings of ACM CHI 2012 Conference on Human Factors in Computing Systems
2012-05-05
v.1
p.569-578
© Copyright 2012 ACM
Summary: This research explores the relationship between self-presentation and
perception by others as manifested explicitly through the use of tags in a
people tagging system. The study provides insights relevant for the
organizational context since it is based on a system implemented within IBM. We
developed a detailed codebook and used it to categorize 9,506 tags assigned to
a sample of taggers. Our analysis examines the use of self tags versus social
tags (assigned by others) across different categories and sub-categories. While
overlap exists, self tags tend to be more factual describing technology
expertise, social tags augment the individual tags by adding a personal
dimension.
Best faces forward: a large-scale study of people search in the enterprise
Search interfaces
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Guy, Ido
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Ur, Sigalit
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Ronen, Inbal
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Weber, Sara
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Oral, Tolga
Proceedings of ACM CHI 2012 Conference on Human Factors in Computing Systems
2012-05-05
v.1
p.1775-1784
© Copyright 2012 ACM
Summary: This paper presents Faces, an application built to enable effective people
search in the enterprise. We take advantage of the popularity Faces has gained
within a globally distributed enterprise to provide an extensive analysis of
how and why people search is used within the organization. Our study is
primarily based on an analysis of the Faces query log over a period of more
than four months, with over a million queries and tens of thousands of users.
The analysis results are presented across four dimensions: queries, users,
clicks, and actions, and lay the foundation for further advancement and
research on the topic.
Diversity among enterprise online communities: collaborating, teaming, and
innovating through social media
Better together
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Muller, Michael
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Ehrlich, Kate
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Matthews, Tara
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Perer, Adam
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Ronen, Inbal
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Guy, Ido
Proceedings of ACM CHI 2012 Conference on Human Factors in Computing Systems
2012-05-05
v.1
p.2815-2824
© Copyright 2012 ACM
Summary: There is a growing body of research into the adoption and use of social
software in enterprises. However, less is known about how groups, such as
communities, use and appropriate these technologies, and the implications for
community structures. In a study of 188 very active online enterprise
communities, we found systematic differences in size, demographics and
participation, aligned with differences in community types. Different types of
communities differed in their appropriation of social software tools to create
and use shared resources, and build relationships. We propose implications for
design of community support features, services for potential community members,
and organizations looking to derive value from online groups.
Personalized activity streams: sifting through the "river of news"
Emerging recommendation domains
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Guy, Ido
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Ronen, Inbal
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Raviv, Ariel
Proceedings of the 2011 ACM Conference on Recommender Systems
2011-10-23
p.181-188
© Copyright 2011 ACM
Summary: Activity streams have emerged as a means to syndicate updates about a user
or a group of users within a social network site or a set of sites. As the
flood of updates becomes highly intensive and noisy, users are faced with a
"needle in a haystack" challenge when they wish to read the news most
interesting to them. In this work, we study activity stream personalization as
a means of coping with this challenge. We experiment with an enterprise
activity stream that includes status updates and news across a variety of
social media applications. We examine an entity-based user profile and a
stream-based profile across three dimensions: people, terms, and places, and
provide a rich set of results through a user study that combines direct rating
of the objects in the profile with rating of the news items it produces.
Digital Traces of Interest: Deriving Interest Relationships from Social
Media Interactions
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Jacovi, Michal
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Guy, Ido
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Ronen, Inbal
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Perer, Adam
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Uziel, Erel
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Maslenko, Michael
Proceedings of the 12th European Conference on Computer-Supported
Cooperative Work
2011-09-24
p.21-40
© Copyright 2011 Springer-Verlag
Summary: Facebook and Twitter have changed the way we consume information, allowing
the people we follow to become our "social filters" and determine the content
of our information stream. The capability to discover the individuals a user is
most interested in following has therefore become an important aspect of the
struggle against information overflow. We argue that the people users are most
interested in following are not necessarily those with whom they are most
familiar. We compare these two types of social relationships -- interest and
familiarity -- inside IBM. We suggest inferring interest relationships from
users' public interactions on four enterprise social media applications. We
study these interest relationships through an offline analysis as well as an
extensive user study, in which we combine people-based and content-based
evaluations. The paper reports a rich set of results, comparing various sources
for implicit interest indications; distinguishing between content-related
activities and status or network updates, showing that the former are of more
interest; and highlighting that the interest relationships include very
interesting individuals that are not among the most familiar ones, and can
therefore play an important role in social stream filtering, especially for
content-related activities.
Do you want to know?: recommending strangers in the enterprise
Enterprise
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Guy, Ido
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Ur, Sigalit
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Ronen, Inbal
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Perer, Adam
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Jacovi, Michal
Proceedings of ACM CSCW'11 Conference on Computer-Supported Cooperative Work
2011-03-19
p.285-294
© Copyright 2011 ACM
Summary: Recent studies on people recommendation have focused on suggesting people
the user already knows. In this work, we use social media behavioral data to
recommend people the user is not likely to know, but nonetheless may be
interested in. Our evaluation is based on an extensive user study with 516
participants within a large enterprise and includes both quantitative and
qualitative results. We found that many employees valued the recommendations,
even if only one or two of nine recommendations were interesting strangers.
Based on these results, we discuss potential deployment routes and design
implications for a stranger recommendation feature.
Social media recommendation based on people and tags
Filtering and recommendation
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Guy, Ido
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Zwerdling, Naama
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Ronen, Inbal
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Carmel, David
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Uziel, Erel
Proceedings of the 33rd Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval
2010-07-19
p.194-201
Keywords: collaborative tagging, personalization, recommender systems, social media,
social networks, social software
© Copyright 2010 ACM
Summary: We study personalized item recommendation within an enterprise social media
application suite that includes blogs, bookmarks, communities, wikis, and
shared files. Recommendations are based on two of the core elements of social
media -- people and tags. Relationship information among people, tags, and
items, is collected and aggregated across different sources within the
enterprise. Based on these aggregated relationships, the system recommends
items related to people and tags that are related to the user. Each recommended
item is accompanied by an explanation that includes the people and tags that
led to its recommendation, as well as their relationships with the user and the
item. We evaluated our recommender system through an extensive user study.
Results show a significantly better interest ratio for the tag-based
recommender than for the people-based recommender, and an even better
performance for a combined recommender. Tags applied on the user by other
people are found to be highly effective in representing that user's topics of
interest.
Same places, same things, same people?: mining user similarity on social
media
He said she said: analyzing interaction patterns
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Guy, Ido
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Jacovi, Michal
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Perer, Adam
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Ronen, Inbal
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Uziel, Erel
Proceedings of ACM CSCW'10 Conference on Computer-Supported Cooperative Work
2010-02-06
p.41-50
Keywords: social media, social networks, social software, user similarity
© Copyright 2010 ACM
Summary: In this work we examine nine different sources for user similarity as
reflected by activity in social media applications. We suggest a classification
of these sources into three categories: people, things, and places. Lists of
similar people returned by the nine sources are found to be highly different
from each other as well as from the list of people the user is familiar with,
suggesting that aggregation of sources may be valuable. Evaluation of the
sources and their aggregates points at their usefulness across different
scenarios, such as information discovery and expertise location, and also
highlights sources and aggregates that are particularly valuable for inferring
user similarity.
Personalized social search based on the user's social network
IR personalization and social search I
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Carmel, David
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Zwerdling, Naama
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Guy, Ido
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Ofek-Koifman, Shila
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Har'el, Nadav
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Ronen, Inbal
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Uziel, Erel
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Yogev, Sivan
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Chernov, Sergey
Proceedings of the 2009 ACM Conference on Information and Knowledge
Management
2009-11-02
p.1227-1236
© Copyright 2009 ACM
Summary: This work investigates personalized social search based on the user's social
relations -- search results are re-ranked according to their relations with
individuals in the user's social network. We study the effectiveness of several
social network types for personalization: (1) Familiarity-based network of
people related to the user through explicit familiarity connection; (2)
Similarity-based network of people "similar" to the user as reflected by their
social activity; (3) Overall network that provides both relationship types. For
comparison we also experiment with Topic-based personalization that is based on
the user's related terms, aggregated from several social applications. We
evaluate the contribution of the different personalization strategies by an
off-line study and by a user survey within our organization. In the off-line
study we apply bookmark-based evaluation, suggested recently, that exploits
data gathered from a social bookmarking system to evaluate personalized
retrieval. In the on-line study we analyze the feedback of 240 employees
exposed to the alternative personalization approaches. Our main results show
that both in the off-line study and in the user survey social network based
personalization significantly outperforms non-personalized social search.
Additionally, as reflected by the user survey, all three SN-based strategies
significantly outperform the Topic-based strategy.
Personalized recommendation of social software items based on social
relations
Tags and social networks
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Guy, Ido
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Zwerdling, Naama
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Carmel, David
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Ronen, Inbal
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Uziel, Erel
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Yogev, Sivan
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Ofek-Koifman, Shila
Proceedings of the 2009 ACM Conference on Recommender Systems
2009-10-23
p.53-60
© Copyright 2009 ACM
Summary: We study personalized recommendation of social software items, including
bookmarked web-pages, blog entries, and communities. We focus on
recommendations that are derived from the user's social network. Social network
information is collected and aggregated across different data sources within
our organization. At the core of our research is a comparison between
recommendations that are based on the user's familiarity network and his/her
similarity network. We also examine the effect of adding explanations to each
recommended item that show related people and their relationship to the user
and to the item. Evaluation, based on an extensive user survey with 290
participants and a field study including 90 users, indicates superiority of the
familiarity network as a basis for recommendations. In addition, an important
instant effect of explanations is found -- interest rate in recommended items
increases when explanations are provided.
Social networks and discovery in the enterprise (SaND)
Demonstrations
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Ronen, Inbal
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Shahar, Elad
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Ur, Sigalit
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Uziel, Erel
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Yogev, Sivan
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Zwerdling, Naama
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Carmel, David
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Guy, Ido
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Har'el, Nadav
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Ofek-Koifman, Shila
Proceedings of the 32nd Annual International ACM SIGIR Conference on
Research and Development in Information Retrieval
2009-07-19
p.836
Keywords: enterprise search, social network, social search
© Copyright 2009 ACM
Do you know?: recommending people to invite into your social network
Recommendations
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Guy, Ido
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Ronen, Inbal
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Wilcox, Eric
Proceedings of the 2009 International Conference on Intelligent User
Interfaces
2009-02-08
p.77-86
Keywords: people recommendations, recommender systems, sns, social networks
© Copyright 2009 ACM
Summary: In this paper we describe a novel UI and system for providing users with
recommendations of people to invite into their explicit enterprise social
network. The recommendations are based on aggregated information collected from
various sources across the organization and are displayed in a widget, which is
part of a popular enhanced employee directory. Recommended people are presented
one by one, with detailed reasoning as for why they were recommended. Usage
results are presented for a period of four months that indicate an extremely
significant impact on the number of connections created in the system.
Responses in the organization's blogging system, a survey with over 200
participants, and a set of interviews we conducted shed more light on the way
the widget is used and implications of the design choices made.
Public vs. private: comparing public social network information with email
Naughty social networking
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Guy, Ido
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Jacovi, Michal
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Meshulam, Noga
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Ronen, Inbal
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Shahar, Elad
Proceedings of ACM CSCW'08 Conference on Computer-Supported Cooperative Work
2008-11-08
p.393-402
© Copyright 2008 ACM
Summary: The goal of this research is to facilitate the design of systems which will
mine and use sociocentric social networks without infringing privacy. We
describe an extensive experiment we conducted within our organization comparing
social network information gathered from various intranet public sources with
social network information gathered from a private source -- the organizational
email system. We also report the conclusions of a series of interviews we
conducted based on our experiment. The results shed light on the richness of
public social network information, its characteristics, and added value over
email network information.
