Social Media Analytics and Summarization

Social Media Analytics and Summarization

Social Media Analytics is the process of “extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision making” (Khan, 2015). Since huge portion of social media data exists in a form of a conversation (e.g. Tweets, forums, etc.), one aspect of analytics is the summarization of these conversations.

Social Media Conversation Summarization

Summarization of social media conversations produces the “Town Hall Summary” that

a) identifies the main issues discussed in a set of reader comments and

b) characterizes opinions offered on these issues,  identifying alternative viewpoints, indicating the strength of interest in an issue or support for different viewpoints (aggregation), indicating consensus or agreement among the comment, indicating disagreement among the comment, indicating qualitatively how opinion was distributed (e.g. using phrases like “Many said this; others said that”, “some said”, “most said”), indicating evidence or grounds for a viewpoint and indicating whether the discussion was particularly emotional/heated and if so over what.

The challenge of producing such summaries is addressed by article-comment linking, topical clustering, cluster labelling and extractive and template-based summarization techniques.

Riccardi G., Bechet F., Danieli M., Favre B., Gaizauskas R., Kruschwitz and Poesio M., ”The SENSEI Project: Making Sense of Human Conversations”, Lecture Notes on Artificial Intelligence, J.F. Quesada et al. ( Eds) , vol. 9577, pp. 10-33, 2016.


Brexit Referendum Use Case

In the month preceding the referendum date, SENSEI’s system monitored millions of social media conversations to predict the outcome of the referendum.

Every day, more than 300,000 posts across multilingual media sources on the topic of the UK EU Referendum are captured and automatically analysed by the SENSEI technology. Most exit polls were showing confidence the REMAIN side would prevail. In contrast, the SENSEI system hit with very high accuracy the final outcome.

Celli F., Stepanov E. A., Poesio M. and Riccardi G., “Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters” , PEOPLES Workshop at , Osaka 2016.

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