Generating associative ripples of relevant information from a variety of data streams by throwing a heuristic stone

Recently, with the widespread popularity of SNS (Social Network Service), such as Twitter, Facebook, people are increasingly accustomed to sharing feeling, experience and knowledge with each other on Internet. The high accessibility of these web sites has allowed the information to be spread across the social media more quickly and widely, which leads to more and more populations being engaged into this so-called social stream environment. All these make the organization of user relationships become increasingly important and necessary. In this study, we try to discover the potential and dynamical user correlations using those organized social streams in accordance with users' current interests and needs, in order to assist the collaborative information seeking process. We develop a heuristic approach to build a Dynamically Socialized User Networking (DSUN) model, and define a set of measures (such as interest degree, and popularity degree) and concepts (such as complementary tie, weak tie, and strong tie), to discover and represent users' current profiling and dynamical correlations. The corresponding algorithms are developed respectively. Finally, the architecture of the functional modules is presented, and the experiment results are demonstrated and discussed based on an application of the proposed model.

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“…The following definitions are proposed and defined for the information seeking process that satisfies users' current interests and needs [36].…”

Section: Metaphors For Social Streamsmentioning

confidence: 99%

“…We have developed two algorithms to assist users' information seeking which can best fit users' current interests and needs [36]. Moreover, we introduced and presented the Dynamically Socialized User Networking (DSUN) Model which can represent users' profiling and relationship in the enhanced social learning environment [37].…”

mentioning

confidence: 99%

A heuristic approach to discovering user correlations from organized social stream data

Zhou

Jin

2014

Multimed Tools Appl

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“…However, the focus of Twitter Search is not on individuals or personal archives and documents may often be ranked based solely on time [2]. Other aspects of searching Twitter have been explored, including event detection [14][17], locating topics of interest [10] [19], and mining opinions [16]. Facebook search emphasizes social relationships, and provides search based mainly on people or organizations.…”

Section: Related Workmentioning

confidence: 99%

Evaluating search in personal social media collections

Lee¹,

Croft²,

Kim³

2012

Proceedings of the Fifth ACM International Conference on Web Search and Data Mining

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The prevalence of social media applications is generating potentially large personal archives of posts, tweets, and other communications. The existence of these archives creates a need for search tools, which can be seen as an extension of current desktop search services. Little is currently known about the best search techniques for personal archives of social data, because of the difficulty of creating test collections. In this paper, we describe how test collections for personal social data can be created by using games to collect queries. We then compare a range of retrieval models that exploit the semi-structured nature of social data. Our results show that a mixture of language models with field distribution estimation can be effective for this type of data, with certain fields, such as the name of the poster, being particularly important. We also analyze the properties of the queries that were generated by users with two versions of the games.

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“…Current: A collection of the drops related to some topics which are formed naturally in the ocean. To seek ripples for users, we have introduced two additional metaphors [26] which are used to describe the seeking process and the results.…”

Section: Metaphors For Social Streamsmentioning

confidence: 99%

Socialized ubiquitous personal study: Toward an individualized information portal

Hong¹,

Zhou²,

Jin³

2012

Journal of Computer and System Sciences

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Recently, SNS (Social Network Service), blog and microblog have become very popular. Stream data, a large collection of diverse contents that are created dynamically in the form of streams, have become an important part of the Internet resources. At the same time, it has become easier to collect people's activities as their lifelogs, not only in the cyber space, but also in the physical world by means of ubiquitous and sensing technology. Either stream data or lifelogs represent different aspects of people's information behaviors and social activities, which we call Social Streams. In this study, we try to integrate and organize these social stream data, such as Twitter Tweets, into Ubiquitous Personal Study (UPS) proposed in our previous study. In this paper, we introduce and define a set of new metaphors: Drop, Stream, River and Ocean, to represent a variety of social stream data in different stages, in order to enable UPS socialized toward an individualized information portal. We further propose a Framework of Organic Streams to meaningfully organize these stream data. We discuss the design and implementation issues of a prototype system, and describe the algorithms to realize our proposed metaphors. Moreover, we show a scenario of using the socialized UPS to support learning activities, with experimental data and analysis results.

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Generating associative ripples of relevant information from a variety of data streams by throwing a heuristic stone

Cited by 12 publications

References 13 publications

A heuristic approach to discovering user correlations from organized social stream data

A heuristic approach to discovering user correlations from organized social stream data

Evaluating search in personal social media collections

Socialized ubiquitous personal study: Toward an individualized information portal

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