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428
Opinion Mining and Sentiment Analysis
, 2008
"... An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, active ..."
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Cited by 711 (3 self)
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An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include materialon summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.
Predicting Positive and Negative Links in Online Social Networks
"... We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinion ..."
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Cited by 225 (7 self)
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We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.
Computing and Applying Trust in Web-based Social Networks
, 2005
"... The proliferation of web-based social networks has lead to new innovations in social networking, particularly by allowing users to describe their relationships beyond a basic connection. In this dissertation, I look specifically at trust in web-based social networks, how it can be computed, and how ..."
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Cited by 200 (16 self)
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The proliferation of web-based social networks has lead to new innovations in social networking, particularly by allowing users to describe their relationships beyond a basic connection. In this dissertation, I look specifically at trust in web-based social networks, how it can be computed, and how it can be used in applications. I begin with a definition of trust and a description of several properties that affect how it is used in algorithms. This is complemented by a survey of web-based social networks to gain an understanding of their scope, the types of relationship information available, and the current state of trust. The computational problem of trust is to determine how much one person in the network should trust another person to whom they are not connected. I present two sets of algorithms for calculating these trust inferences: one for networks with binary trust ratings, and one for continuous ratings. For each rating scheme, the algorithms are built upon the defined notions of trust. Each is then analyzed theoretically and with respect to simulated and actual trust networks to determine how accurately they calculate the opinions of people in the system. I show that in both rating schemes the algorithms
Finding high-quality content in social media with an application to community-based question answering
- In Proceedings of WSDM
, 2008
"... The quality of user-generated content varies drastically from excellent to abuse and spam. As the availability of such content increases, the task of identifying high-quality content in sites based on user contributions—social media sites— becomes increasingly important. Social media in general exhi ..."
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Cited by 167 (14 self)
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The quality of user-generated content varies drastically from excellent to abuse and spam. As the availability of such content increases, the task of identifying high-quality content in sites based on user contributions—social media sites— becomes increasingly important. Social media in general exhibit a rich variety of information sources: in addition to the content itself, there is a wide array of non-content information available, such as links between items and explicit quality ratings from members of the community. In this paper we investigate methods for exploiting such community feedback to automatically identify high quality content. As a test case, we focus on Yahoo! Answers, a large community question/answering portal that is particularly rich in the amount and types of content and social interactions available in it. We introduce a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition. In particular, for the community question/answering domain, we show that our system is able to separate high-quality items from the rest with an accuracy close to that of humans. Categories and Subject Descriptors H.3 [Information Storage and Retrieval]: H.3.1 Content Analysis and Indexing – indexing methods, linguistic
A content-driven reputation system for the Wikipedia
- In Proceedings of the 16th International World Wide Web Conference
, 2007
"... On-line forums for the collaborative creation of bodies of information are a phenomenon of rising importance; the Wikipedia is one of the best-known examples. The open nature of such forums could benet from a notion of reputation for its authors. Author reputation could be used to
ag new contributi ..."
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Cited by 159 (11 self)
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On-line forums for the collaborative creation of bodies of information are a phenomenon of rising importance; the Wikipedia is one of the best-known examples. The open nature of such forums could benet from a notion of reputation for its authors. Author reputation could be used to
ag new contributions from low-reputation authors, and it could be used to allow only authors with good reputation to contribute to controversial or critical pages. A reputation system for the Wikipedia would also provide an incentive to give high-quality contributions. We present in this paper a novel type of content-driven reputation system for Wikipedia authors. In our system, authors gain reputation when the edits and text additions they perform to Wikipedia articles are long-lived, and they lose reputation when their changes are undone in short order. We have implemented the pro-posed system, and we have used it to analyze the en-tire Italian and French Wikipedias, consisting of a to-tal of 691,551 pages and 5,587,523 revisions. Our re-sults show that our notion of reputation has good pre-dictive value: changes performed by low-reputation au-thors have a signicantly larger than average probability of having poor quality, and of being undone. 1
Sybilproof Reputation Mechanisms
- In Proceedings of the ACM Workshop on Economics of Peer-to-Peer Systems (P2PECON
, 2005
"... Due to the open, anonymous nature of many P2P networks, new identities- or sybils- may be created cheaply and in large numbers. Given a reputation system, a peer may at-tempt to falsely raise its reputation by creating fake links between its sybils. Many existing reputation mechanisms are not resist ..."
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Cited by 147 (2 self)
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Due to the open, anonymous nature of many P2P networks, new identities- or sybils- may be created cheaply and in large numbers. Given a reputation system, a peer may at-tempt to falsely raise its reputation by creating fake links between its sybils. Many existing reputation mechanisms are not resistant to these types of strategies. Using a static graph formulation of reputation, we at-tempt to formalize the notion of sybilproofness. We show that there is no symmetric sybilproof reputation function. For nonsymmetric reputations, following the notion of repu-tation propagation along paths, we give a general asymmet-ric reputation function based on flow and give conditions for sybilproofness.
Learning Influence Probabilities In Social Networks
"... Recently, there has been tremendous interest in the phenomenon of influence propagation in social networks. The studies in this area assume they have as input to their problems a social graph with edges labeled with probabilities of influence between users. However, the question of where these proba ..."
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Cited by 145 (18 self)
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Recently, there has been tremendous interest in the phenomenon of influence propagation in social networks. The studies in this area assume they have as input to their problems a social graph with edges labeled with probabilities of influence between users. However, the question of where these probabilities come from or how they can be computed from real social network data has been largely ignored until now. Thus it is interesting to ask whether from a social graph and a log of actions by its users, one can build models of influence. This is the main problem attacked in this paper. In addition to proposing models and algorithms for learning the model parameters and for testing the learned models to make predictions, we also develop techniques for predicting the time by which a user may be expected to perform an action. We validate our ideas and techniques using the Flickr data set consisting of a social graph with 1.3M nodes, 40M edges, and an action log consisting of 35M tuples referring to 300K distinct actions. Beyond showing that there is genuine influence happening in a real social network, we show that our techniques have excellent prediction performance.
Trust-aware Collaborative Filtering for Recommender Systems
- In Proc. of Federated Int. Conference On The Move to Meaningful Internet: CoopIS, DOA, ODBASE
, 2004
"... Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. ..."
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Cited by 136 (5 self)
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Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours.
Towards the semantic web: Collaborative tag suggestions
- Proceedings of Collaborative Web Tagging Workshop at 15th International World Wide Web Conference
, 2006
"... Content organization over the Internet went through several interesting phases of evolution: from structured directories to unstructured Web search engines and more recently, to tagging as a way for aggregating information, a step towards the semantic web vision. Tagging allows ranking and data orga ..."
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Cited by 136 (0 self)
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Content organization over the Internet went through several interesting phases of evolution: from structured directories to unstructured Web search engines and more recently, to tagging as a way for aggregating information, a step towards the semantic web vision. Tagging allows ranking and data organization to directly utilize inputs from end users, enabling machine processing of Web content. Since tags are created by individual users in a free form, one important problem facing tagging is to identify most appropriate tags, while eliminating noise and spam. For this purpose, we define a set of general criteria for a good tagging system. These criteria include high coverage of multiple facets to ensure good recall, least effort to reduce the cost involved in browsing, and high popularity to ensure tag quality. We propose a collaborative tag suggestion algorithm using these criteria to spot high-quality tags. The proposed algorithm employs a goodness measure for tags derived from collective user authorities to combat spam. The goodness measure is iteratively adjusted by a reward-penalty algorithm, which also incorporates other sources of tags, e.g., content-based auto-generated tags. Our experiments based on My Web 2.0 show that the algorithm is effective.
A Survey of Trust in Computer Science and the Semantic Web
, 2007
"... Trust is an integral component in many kinds of human interaction, allowing people to act under uncertainty and with the risk of negative consequences. For example, exchanging money for a service, giving access to your property, and choosing between conflicting sources of information all may utilize ..."
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Cited by 135 (3 self)
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Trust is an integral component in many kinds of human interaction, allowing people to act under uncertainty and with the risk of negative consequences. For example, exchanging money for a service, giving access to your property, and choosing between conflicting sources of information all may utilize some form of trust. In computer science, trust is a widelyused term whose definition differs among researchers and application areas. Trust is an essential component of the vision for the Semantic Web, where both new problems and new applications of trust are being studied. This paper gives an overview of existing trust research in computer science and the Semantic Web.
