From Tweets to Stories: Using Stream-Dashboard to weave the twitter data stream into dynamic cluster models

Basheer Hawwash, Olfa Nasraoui
Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, PMLR 36:182-197, 2014.

Abstract

Social media has recently emerged as an invaluable source of information for decision making. Social media information reflects the interests of virtual communities in a spontaneous and timely manner. The need to understand the massive streams of data generated by social media platforms, such as Twitter and Facebook, has motivated researchers to use machine learning techniques to try to discover knowledge in real time. In this paper, we adapt our recently developed stream cluster mining, tracking and validation framework, Stream-Dashboard, to support detecting and tracking evolving discussion clusters in Twitter. The effectiveness of Stream-Dashboard in telling stories is illustrated by analyzing a couple of stories related to the Louisville Cardinals’ basketball championship. We further validate the detected story lines, that are automatically mined from user-generated tweets using as an alternative source, Google Trends, which are based on search queries.

Cite this Paper

BibTeX
@InProceedings{pmlr-v36-hawwash14, title = {From Tweets to Stories: Using Stream-Dashboard to weave the twitter data stream into dynamic cluster models}, author = {Hawwash, Basheer and Nasraoui, Olfa}, booktitle = {Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications}, pages = {182--197}, year = {2014}, editor = {Fan, Wei and Bifet, Albert and Yang, Qiang and Yu, Philip S.}, volume = {36}, series = {Proceedings of Machine Learning Research}, address = {New York, New York, USA}, month = {24 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v36/hawwash14.pdf}, url = {https://proceedings.mlr.press/v36/hawwash14.html}, abstract = {Social media has recently emerged as an invaluable source of information for decision making. Social media information reflects the interests of virtual communities in a spontaneous and timely manner. The need to understand the massive streams of data generated by social media platforms, such as Twitter and Facebook, has motivated researchers to use machine learning techniques to try to discover knowledge in real time. In this paper, we adapt our recently developed stream cluster mining, tracking and validation framework, Stream-Dashboard, to support detecting and tracking evolving discussion clusters in Twitter. The effectiveness of Stream-Dashboard in telling stories is illustrated by analyzing a couple of stories related to the Louisville Cardinals’ basketball championship. We further validate the detected story lines, that are automatically mined from user-generated tweets using as an alternative source, Google Trends, which are based on search queries.} }
Endnote
%0 Conference Paper %T From Tweets to Stories: Using Stream-Dashboard to weave the twitter data stream into dynamic cluster models %A Basheer Hawwash %A Olfa Nasraoui %B Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications %C Proceedings of Machine Learning Research %D 2014 %E Wei Fan %E Albert Bifet %E Qiang Yang %E Philip S. Yu %F pmlr-v36-hawwash14 %I PMLR %P 182--197 %U https://proceedings.mlr.press/v36/hawwash14.html %V 36 %X Social media has recently emerged as an invaluable source of information for decision making. Social media information reflects the interests of virtual communities in a spontaneous and timely manner. The need to understand the massive streams of data generated by social media platforms, such as Twitter and Facebook, has motivated researchers to use machine learning techniques to try to discover knowledge in real time. In this paper, we adapt our recently developed stream cluster mining, tracking and validation framework, Stream-Dashboard, to support detecting and tracking evolving discussion clusters in Twitter. The effectiveness of Stream-Dashboard in telling stories is illustrated by analyzing a couple of stories related to the Louisville Cardinals’ basketball championship. We further validate the detected story lines, that are automatically mined from user-generated tweets using as an alternative source, Google Trends, which are based on search queries.
RIS
TY - CPAPER TI - From Tweets to Stories: Using Stream-Dashboard to weave the twitter data stream into dynamic cluster models AU - Basheer Hawwash AU - Olfa Nasraoui BT - Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications DA - 2014/08/13 ED - Wei Fan ED - Albert Bifet ED - Qiang Yang ED - Philip S. Yu ID - pmlr-v36-hawwash14 PB - PMLR DP - Proceedings of Machine Learning Research VL - 36 SP - 182 EP - 197 L1 - http://proceedings.mlr.press/v36/hawwash14.pdf UR - https://proceedings.mlr.press/v36/hawwash14.html AB - Social media has recently emerged as an invaluable source of information for decision making. Social media information reflects the interests of virtual communities in a spontaneous and timely manner. The need to understand the massive streams of data generated by social media platforms, such as Twitter and Facebook, has motivated researchers to use machine learning techniques to try to discover knowledge in real time. In this paper, we adapt our recently developed stream cluster mining, tracking and validation framework, Stream-Dashboard, to support detecting and tracking evolving discussion clusters in Twitter. The effectiveness of Stream-Dashboard in telling stories is illustrated by analyzing a couple of stories related to the Louisville Cardinals’ basketball championship. We further validate the detected story lines, that are automatically mined from user-generated tweets using as an alternative source, Google Trends, which are based on search queries. ER -
APA
Hawwash, B. & Nasraoui, O.. (2014). From Tweets to Stories: Using Stream-Dashboard to weave the twitter data stream into dynamic cluster models. Proceedings of the 3rd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications, in Proceedings of Machine Learning Research 36:182-197 Available from https://proceedings.mlr.press/v36/hawwash14.html.

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