Complementary integration of heterogeneous crowd-sourced datasets for enhanced social analytics

Ryong Lee, Kyoung Sook Kim, Komei Sugiura, Koji Zettsu, Yutaka Kidawara

Research output: Contribution to journalConference articlepeer-review

2 Citations (Scopus)


On behalf of the rapidly and widely disseminated smartphone technology into the public, lots of social network sites and location-based social applications are accumulating a huge volume of massive crowd's daily experiences and thoughts in an unprecedented scale. We can regard them as novel data sources for accomplishing various social analytics, which have usually required lots of efforts to collect crowds'f opinion and behavioral data. Thus, we can take advantages of abundant social datasets by integrating them appropriately. However, when we integrate disparate sources to derive a comprehensive view for a survey, it is necessary to know intrinsic exclusive values of each data source compared to others in an intuitive and succinct way. In fact, lots of efforts and time are wasted to overview various datasets consequently to confidently choose a dataset to be integrated in a final result. In this paper, we propose a complementarity index, which can estimate the exclusive usefulness of data sources in terms of spatial and topical coverage when selecting data sources for social analytics purposes. We conducted an experiment about complementarity measurement with two real social datasets from Twitter and VoiceTra, the latter is a speech-to-speech translation app, with which we can additionally obtain crowds' verbal translation logs. With the proposed complementarity index, we can measure the capability of a dataset comparing to others before integrating datasets, thus enabling analysts to examine much more datasets from as many related data sources as possible by focusing on exclusive coverage and relative strength of relevant topics.

Original languageEnglish
Article number6569096
Pages (from-to)234-243
Number of pages10
JournalProceedings - IEEE International Conference on Mobile Data Management
Publication statusPublished - 2013
Externally publishedYes
Event14th International Conference on Mobile Data Management, MDM 2013 - Milan, Italy
Duration: 2013 Jun 32013 Jun 6


  • Complementarity Measumement
  • Crowd Lifelogs
  • Mobile Applications
  • Social Analysis

ASJC Scopus subject areas

  • General Engineering


Dive into the research topics of 'Complementary integration of heterogeneous crowd-sourced datasets for enhanced social analytics'. Together they form a unique fingerprint.

Cite this