An educated and informed consumption of media content has become a challenge in modern times.With the shift from traditional news outlets to social media and similar venues, a major concern is that readers are becoming encapsulated in "echo chambers" and may fall prey to fake news and disinformation, lacking easy access to dissenting views.We suggest a novel task aiming to alleviate some of these concerns -that of detecting articles that most effectively counter the arguments -and not just the stance -made in a given text.We study this problem in the context of debate speeches.Given such a speech, we aim to identify, from among a set of speeches on the same topic and with an opposing stance, the ones that directly counter it.We provide a large dataset of 3685 such speeches (in English), annotated for this relation, which hopefully would be of general interest to the NLP community.We explore several algorithms addressing this task, and while some are successful, all fall short of expert human performance, suggesting room for further research.All data collected during this work is freely available for research 1 .

Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Shachar Mirkin, Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018.

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'.

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.

Enterprise social media can provide visibility of users' actions and thus has the potential to reveal insights about users in the organization. We mined large-scale social media use in an enterprise to examine: a) user roles with such broad platforms and b) whether people with large social networks are highly regarded. First, a factor analysis revealed that most variance of social media usage is explained by commenting and 'liking' behaviors while other usage can be characterized as patterns of distinct tool usage. These results informed the development of a model showing that online network size interacts with other media usage to predict who is highly assessed in the organization. We discovered that the smaller one's online social network size in the organization, the more highly assessed they were by colleagues. We explain this inverse relationship as due to friending behavior being highly visible but not yet valued in the organization.

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.

As people continue to author and share increasing amounts of information in social media, the opportunity to leverage such information for relationship discovery tasks increases. In this paper, we describe a set of systems that mine, aggregate, and infer a social graph from social media inside an enterprise, resulting in over 73 million relationships between 450,000 people. We then describe SaNDVis, a novel visual analytics tool that supports people-centric tasks like expertise location, team building, and team coordination in the enterprise. We provide details of a 22-month-long, large-scale deployment to over 2,300 users from which we analyze longitudinal usage patterns, classify types of visual analytics queries and users, and extract dominant use cases from log and interview data. By integrating social position, evidence, and facets into SaNDVis, we demonstrate how users can use a visual analytics tool to reflect on existing relationships as well as build new relationships in an enterprise setting.

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.

The Third Workshop on Social Media (WSM2011) continues the series of Workshops on Social Media in 2009 and 2010 and has been established as a platform for the presentation and discussion of the latest, key research issues in social media analysis, exploration, search, mining, and emerging new social media applications. It is held in conjunction with the ACM International Multimedia Conference (MM'11) at Scottsdale, Arizona, USA, 2011 and has attracted contributions on various aspects of social media including data mining from social media, content organization, geo-localization, personalization, recommendation systems, user experience, machine learning and social media approaches and architectures for large-scale data processing.

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.

Intelligent Environments most commonly take a physical form such as homes, offices, hotels, restaurants, shops, that are equipped with advanced networked computer based systems, which enable better or new lifestyles for people. However, Intelligent Environments can also take the form of virtual online spaces such as SecondLife, which can both mimic the real world and provide functionalities which could not be provided in reality, such as advanced simulations and movement. There is the growing trend for people to spend more time in such virtual environments and, to these ends, this work in progress paper reports on a new project, +Spaces which is developing a range of virtual world tools for e-government applications, and presents some of the concepts and technical challenges involved in creating these intelligent virtual spaces for e-government.

Feed readers have emerged as one of the salient applications that characterize Web 2.0. Lately, some of the available readers introduced social features, analogously to other Web 2.0 applications, such as recommendations and tagging. Yet, most of the readers lack collaborative features, such as the ability to share feeds in a community or divide the reading task among community members. In this paper we describe CoffeeReader, a web-based feed reader, which combines social and collaborative features, and is deployed in a small community within our company. CoffeeReader provides awareness of other users' feed lists and read status; it enables information sharing such as tags and recommendations; and aims to support coordination of filtering through feeds to locate important items. We compare these group collaboration features of CoffeeReader with emerging features in publicly available feed readers; present the outcomes of using CoffeeReader within our community; and discuss our findings and their implications on making feed readers more collaborative.

Social network sites rely on the contributions of their members to create a lively and enjoyable space. Recent research has focused on using personalization and recommender technologies to encourage participation of existing members. In this work we present an early-intervention approach to encouraging participation and engagement, which makes recommendations to new users during their sign-up process. Our recommender system exploits external social media to produce people and profile entry recommendations for new users. We present results of a live user study, showing that users who received recommendations at sign-up created more social connections, contributed more content, and were on the whole more engaged with the system, contributing more without prompt and returning more often. We further show that recommendations for multiple content types yield significantly better results, in terms of user contribution and consumption; and that recommendations of more active users yield a higher return rate.

Web 2.0 gives people a substantial role in content and metadata creation. New interpersonal connections are formed and existing connections become evident through Web 2.0 services. This newly created social network (SN) spans across multiple services and aggregating it could bring great value. In this work we present SONAR, an API for gathering and sharing SN information. We give a detailed description of SONAR, demonstrate its potential value through user scenarios, and show results from experiments we conducted with a SONAR-based social networking application. These suggest that aggregating SN information across diverse data sources enriches the SN picture and makes it more complete and useful for the end user.

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.

The CSCW conference is celebrating its 20th birthday. This is a perfect time to analyze the coherence of the field, to examine whether it has a solid core or sub-communities, and to identify various patterns of its development. In this paper we analyze the structure of the CSCW conference using structural analysis of the citation graph of CSCW and related publications. We identify the conference's core and most prominent clusters. We also define a measure to identify chasm-papers, namely papers cited significantly more outside the conference than within, and analyze such papers.

While virtual communities become more and more dominant, little attention has been directed towards understanding the conditions for creating a successful community. Significant progress has been made in understanding the diffusion of collaborative tools in the workplace. We read stories about the extraordinary success of some communities, and about the harsh failure of others. This paper argues that lessons learnt from these stories should be analyzed using the theoretical foundations of Diffusion of Innovations theories, and systematized to create a set of guidelines for community creators to make their efforts more efficient. We begin by presenting a theoretical background for analyzing technology diffusion. We then analyze the stories of diffusion of ReachOut - a tool for peer support and community building developed in our Research Lab - in two different communities, using this theory. Finally, we propose a framework for planning for successful diffusion of collaborative tools, using our experiences with ReachOut.

Collaboration plays a vital role in today's new business environment. Knowledge that resides within people's heads has become an invaluable resource. Many formal tools, such as e-mail or teamrooms, have been introduced to support formal collaboration and have been studied extensively. However, support for informal communication is still in its infancy. Much work has been done to analyze the functions that informal communication plays in the workplace. Recently, several studies have evaluated the roles that instant messaging (IM) plays in similar settings. Research shows that in the workplace, IM is used primarily for work-related purposes and accelerates the completion of important business tasks. Clearly, new tools that combine both formal and informal interaction can bring organizations tremendous rewards. ReachOut is a tool for semi-persistent collaboration and peer support developed by the Collaboration Technologies Group at the IBM Haifa Research Lab. This paper studies the role ReachOut plays in the workplace. We analyzed the collaboration activity of the community of IBM Haifa Labs employees who used ReachOut for a period of two months. As a result, we summarize the important functions played by tools that bridge between formal and informal communication in a workplace-based community.

This paper presents ReachOut, a chat-based tool for peer support, collaboration, and community building. We describe the philosophy behind the tool and explain how posting questions in the open directly benefits the creation, distribution, and use of organizational knowledge, in addition to enhancing the cohesion of the community involved. ReachOut proposes new methods of handling problems that include locating, selecting, and approaching the right set of potential advisers. We discuss the advantages of public discussions over private, one-on-one sessions, and how this is enhanced by our unique combination of synchronous and asynchronous communication. We present and analyze results from a pilot of ReachOut and conclude with plans for future research and development.

Article On learning in the limit and non-uniform (ε,δ)-learning Share on Authors: Shai Ben-David View Profile , Michal Jacovi View Profile Authors Info & Claims COLT '93: Proceedings of the sixth annual conference on Computational learning theoryAugust 1993 Pages 209–217https://doi.org/10.1145/168304.168333Online:01 August 1993Publication History 3citation155DownloadsMetricsTotal Citations3Total Downloads155Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access