Finding someone in my social directory whom i do not fully remember or barely know

Zhou, Michelle X.; Zhang, Wei; Smith, Barton A.; Varga, Erika; Farias, Martin; Badenes, Hernan

doi:10.1145/2166966.2167001

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…For instance, replies and retweets can provide information about the topics users follow their friends for. Similar work was performed by [25] where topics were extracted from discussions between users in a social network.…”

Section: Discussionmentioning

confidence: 99%

CatStream

Esparza

O’Mahony

Smyth

2013

Proceedings of the 2013 International Conference on Intelligent User Interfaces

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Real-time information streams such as Twitter have become a common way for users to discover new information. For most users this means curating a set of other users to follow. However, at the moment the following granularity of Twitter is restricted to the level of individual users. Our research has highlighted that many following relationships are motivated by a subset of interests that are shared by the users in question. For example, user A might follow user B because of their technology related tweets, but shares little or no interest in their other tweets. As a result, this all-or-nothing following relationship can quickly overwhelm users' timelines with extraneous information. To improve this situation we propose a user profiling approach based on the topical categorisation of users' posted URLs. These topics can then be used to filter information streams so that they focus on more relevant information from the people they follow, based on their core interests. In particular, we present a system called CatStream that provides for a more fine-grained way to follow users on specific topics and filter our timelines accordingly. We present the results of a live-user study that shows how filtered timelines offer a better way to organise and filter their information streams. Most importantly users are generally satisfied with the categories predicted for their profiles and tweets.

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Section: Discussionmentioning

confidence: 99%

CatStream

Esparza

O’Mahony

Smyth

2013

Proceedings of the 2013 International Conference on Intelligent User Interfaces

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“…Other interesting works focus on some specific aspects of user profiles : (1) Modeling user interests, for example (Abel et al, 2011;Orlandi et al, 2012) by combing user information profiling and the Semantic Web (especially using DBpedia) ; (2) Utilizing user preferences for collaborative recommender systems (Shapira et al, 2012) ; (3) Modelling user expertises and weighting user relationships for social search engine (Zhou et al, 2012), (Vu and Baid, 2012).…”

Section: Related Workmentioning

confidence: 99%

“…As the number of Friends may grow so rapidly that it becomes impossible to sort and filter all heterogeneous information from Friends profiles according to the user's current interest. To cope with this problem some cross-system tools have been developed to give users useful information such as recommendation systems (Abel et al, 2011), social search services (Zhou et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

User-centered Social Network Profiles Integration

2013

Proceedings of the 9th International Conference on Web Information Systems and Technologies

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Large scale online social networks (OSNs) such as Facebook, Twitter, LinkedIn, have become an important part of our every day life. Users are connected to multiple OSNs in which they maintain their different profiles including a lot of personal and social information. As the number of friends of a given user may grow so rapidly, it becomes impossible to manage all updates from friends' profiles to filter relevant information. We present a FOAF-based profiles aggregation model, which is able to align different user profiles available on OSNs into aggregated profiles within a single triple store. The aggregated profiles are then linked together by friend connection. We illustrate the applicability by the presentation of some applications supposed to provide users some effective help in information searching.

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“…pipl 1 ) and research projects (e.g. [2] [11]) were created in order to find and compile information about people. Moreover, previous research reported that traditional search engines are regularly used to search for people [8].…”

Section: Introductionmentioning

confidence: 99%

"Tell me what I want to know!"

Guerreiro

Gonçalves

2013

Proceedings of the 24th ACM Conference on Hypertext and Social Media

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The growing amount of personal information on the web raises increasing concerns about what and with whom we share information online. Nevertheless, little effort has been made in determining the relevance of the information shared with us or in filtering it accordingly. We conducted a study to identify the most relevant characteristics when seeking information about people and to scrutinize their differences among relationship types. To achieve that, we asked users to describe people (friends, acquaintances and famous people). Afterwards, we asked them to rate the perceived relevance of a carefully pre-determined set of attributes for each type. Results showed that their relevance varied depending on the relationship. As an outcome, we present the most relevant attributes when seeking information about friends, acquaintances and famous people and the major differences among them. We conclude suggesting how our findings may influence the design of interactive systems where such data is paramount.

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Finding someone in my social directory whom i do not fully remember or barely know

Cited by 9 publications

References 9 publications

CatStream

CatStream

User-centered Social Network Profiles Integration

"Tell me what I want to know!"

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