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TEDxAthens 2011 insights

1 Mar 2012   likes: 1  permalink   tags: tedxathens  art of disruption  insights  ted 

The TEDxAthens 2011 theme was “the Art of Disruption”. The conference took place in Athens, on  December 3rd 2011 with great success. Together with the TEDxAthens team and the Civitas team we have analyzed the 9323 social media references to TEDxAthens 2011 covering the entire week of the event.

The analysis has produced a variety of insights, such as:

Calculation of buzz; segmentation of the conference to time periods corresponding to the speakers; detection of the top quotes for each user, as well as the top quotes in general, as these were tweeted or re-tweeded by the audience; computation of the links to web sites, blogs and photo sharing sites that were shared by users; identification of the most popular nodes of the network that was formed around the event; and computation of the aspects of the discussion, that is the key-words, phrases and terms with the most discriminative capacity and which better characterized the discussion about TEDxAthens.

We have linguistically processed the “About” fields that are included in the profiles of the 100 most important nodes of the network that was developed around the event and we have created the following word cloud.

TedxAthens2011

A beautiful video of the insights that characterized the event can be found here: http://blog.tedxathens.com/tedx-athens-2011-social-media-video-infographic  

The report can be found here: http://dl.dropbox.com/u/153498/TEDxAth/2011/TEDx%20Athens%20social%20media%20buzz%20analysis.pdf

Our warmest thanks to the TEDx Athens team and the Civitas team for this wonderful collaboration ! 

Twitter engaged community: sharing links to social media

30 Sep 2011   likes: 25  permalink   tags: twitter  social media  community  engagement  link sharing 

This is the last post in the series on community engagement. 

People use Twitter to share links. In June 2011 almost 200 million tweets were being sent per day and 25% of these tweets contained links. It seems that content sharing is a secret to Twitter success. 

The theme of our last infographic is sharing links to social media. Thank you for your comments, remarks and ideas. There is more to come !

Social media twitter community

Twitter engaged community: sharing links to world media sites

29 Sep 2011   likes: 17  permalink   tags: twitter  foreign media sites  engagement  community  link sharing 

Which are the most popular world media sites? And how many unique users post links to those sites?

foreign media sites twitter community

Twitter engaged community: sharing links to Greek media sites

28 Sep 2011   likes: 10  permalink   tags: Twitter  engagement  link sharing  Greek media  community 

Which are the most popular media sites? And how many unique users post links to those sites?

Greek media sites twitter community

Twitter engaged community: sharing links to lifestyle sites

27 Sep 2011   likes: 5  permalink   tags: twitter  engagement  community  link sharing  lifestyle sites 

Which are the most popular lifestyle sites? And how many unique users post links to those sites?

Greek lifestyle sites twitter community

Twitter engaged community: sharing links to sport sites

  likes: 7  permalink   tags: twitter  engagement  community  sport sites  link sharing 

Which are the most popular sport sites? And how many unique users post links to those sites?

Greek sport sites twitter community

Twitter engaged community: sharing links to government sites

26 Sep 2011   likes: 14  permalink   tags: twitter  engagement  community  government  link sharing 

Which are the most popular government sites? And how many unique users post links to those sites?

Greece government sites twitter community

Twitter engaged community: sharing links to sites of political parties

  likes: 21  permalink   tags: twitter  engagement  community  political parties  link sharing 

People often tweet links to content on web pages. Which are the most popular pages? And how many unique users post links to those pages?

We have computed the number of unique Greek users that post links to web sites and we have ranked the sites accordingly. The engaged community of a web site is a strong indicator of the site’s impact. This is part of the deeper analysis that Qualia performs on the characteristics of such communities.

Period: 01 January - 15 September 2011.


Greek Political Parties Sites Twitter Community

Opinion Aspects: the case of President Barack Obama

1 Aug 2011   likes: 22  permalink   tags: Sentiment Analysis  Opinion Mining  Subjectivity  Polarity  Obama  Greek crisis  Clinton 

July 2011 has been a difficult month for President Obama and his administration, with many crucial issues running in parallel. In what concerns Greece, the President has welcomed the important steps taken to stabilize the euro zone under the new Greek debt deal. Also, regarding the american debt crisis, Barack Obama declared on July 15 that “we are not Greece”. Moreover, Hillary Clinton, the US Secretary of State, expressed her support for Greece during a visit to Athens, saying: “We stand by the people and the government of Greece”. We could also mention the ads that appeared at various stations and bus stops in Washington, D.C. that discredited Greece with the slogan “Next Stop: Greece. Raising the debt without cutting spending is our own Greek Tragedy”.

What is the opinion on Barack Obama among Greek bloggers? Opinion analysis is a new feature included in the latest release of aino. We have analyzed opinion for President Obama on Greek blogs, for the period July 1 – July 28, 2011. We start our presentation of the findings by showing the number of mentions on a daily basis:

Obama posts volume

Here is in short the procedure that we have implemented in order to compute opinion within texts. We start by creating the entity “Barack Obama” and searching for its mentions in very large corpora from Greek news sites and blogs. Next, we train our model in order to form a conceptual cloud around the entity that will make possible the linking between, for instance, Barack Obama, President Obama and the President of the U.S. We have manually created sentiment resources in Greek and we match our dictionaries against the input documents. Next, we apply natural language processing techniques in order to compute whether sentiment expressions actually refer to the entity “Barack Obama”. Once we find associations, we apply rules such as negation in order to determine the intensity and polarity of the expression. The next diagram depicts the opinion over time on President Obama, in the range [-1…+1] (very negative to very positive).

Obama opinion analysis trend

We can see that opinion on President Obama in Greek blogs has been negative during July 2011. The main events that contributed to the negative opinion were:

On July 8, the President blamed Greece, Japan, high gas prices, uncertainty over the debt-ceiling issue, and natural disasters across the globe for the rising US unemployment.

On July 12, Le Monde in its editorial with the title “Ces gamins qui nous gouvernent” (those kids that govern us) declared: la situation requiert des adultes - et on en manque, à Washington comme à Bruxelles http://www.lemonde.fr/idees/article/2011/07/12/ces-gamins-qui-nous-gouvernent_1547754_3232.html. The editorial was extensively reproduced in Greek on-line sources.

On July 15, the President said the US is not Greece. During the same period appeared the offensive campaign against Greece: Next Stop: Greece, Don’t raise the debt ceiling without cutting spending.

On July 22, the US debt limit talks failed.

The only positive event occurred on July 18, when the US Secretary of the State Hillary Clinton visited Greece and received positive media comments, as she declared the support of Washington in the efforts made by the Greek government to tackle the economic crisis.

The distribution of opinion in ranges of values is as follows:

Obama sentiment analysis distribution

 The majority of values fit in the range [-0,20…0]. Very positive values are associated with Mrs. Clinton’s visit in Athens and the final flight of NASA’s space shuttle program, an event covered by Greek media.

There are two more dimensions of opinion analysis that are computed by aino: polarity and subjectivity. Positive and negative affect theories are based on the idea that positive and negative opinion should be separately tracked because they vary independently. An aggregate neutral opinion can be the result of equal antithetical opinions of low or high polarity. The evolution of polarity over time appears in the next diagram:

Obama sentiment analysis polarity

Some high polarity points are associated with the following events:

On July 4 a Fox news hacker announced that Obama is dead.

On July 8 the President blamed Greece for the spike in the US unemployment figures.

On July 23 several events took place: the Norway attack and the war on terrorism, House Speaker John Boehner’s decision to end debt talks with President Barack Obama, and an article signed by Paul Krugman that appeared in the Times and was reproduced in Greek media, entitled “letting bankers walk” http://www.nytimes.com/2011/07/18/opinion/18krugman.html.

The computational processing of subjectivity within texts is also a feature of the opinion analysis family. The idea is to compute the amount of subjective expressions within a document.

Obama sentiment analysis subjectivity

On July 11, Mrs. Lagarde, the new chief of IMF foresees “real nasty consequences” if the U.S. fails to raise its borrowing limit http://www.huffingtonpost.com/2011/07/10/imf-us-borrowing-limit_n_894044.html. Subjectivity exhibits local maxima on this date because Mrs. Lagarde’s statement has ignited a discussion with subjective arguments and critics that dismissed Greece’s butterfly effect role in the global crisis, since the US debt crisis is order of magnitudes more severe than the Greek debt crisis.

Finally, July 23 (see above) seems to be a day with very negative opinion, high polarity and high subjectivity as well.

Tracking the Norway attack

25 Jul 2011   likes: 24  permalink   tags: clustering  topic tracking  topic trend  Utoya  Oslo  Norway 

The attack on a Norwegian island and the massive explosion in the heart of Oslo shocked the world. The devastating incidents took place on Friday July 22, and quickly climbed to the top of the news. The impact of the event as well as its share-of-voice are very high.

Topic detection and tracking is a technology that organizes a stream of constantly arriving news stories by the events that they discuss. Aino has detected the new event by using an advanced clustering algorithm that automatically creates a bundle, that is a multimedia topic aggregating information atoms from real-time multimedia streams regarding the new event. A topic is defined to be a seminal event or activity, along with all directly related events and activities.

Oslo Norway attack topic big picture

The next picture taken from aino presents the details of the bundle by showing its summary, its term cloud and its mentions from the multimedia information sources that are followed by aino: mainstream news, blogs, twitter and broadcasts.

Oslo Norway attack topic details

Major Greek TV channels interrupted normal programming with a breaking news report on the event. Aino applies automatic speech recognition in broadcast news in order to identify the mentions regarding the event. Moreover, aino applies video text recognition on overlay text and retrieves relevant video segments by searching the multimedia index. This methodology significantly improves the precision of the retrieval procedure.

Oslo Norway attack tv clips

Bloggers and twitters almost immediately covered the attacks.

Oslo Norway attack greek blogs

 

Oslo Norway attack greek tweets

Once we have detected the topic and we have created its corresponding topic model, our aim is to map incoming information atoms to the predefined topic, so that we can track its evolution over time. Aino monitors the stream of arriving news stories and picks out those that discuss the Norway attacks. The topic model is updated with each new information atom that is assigned to the topic. The retrieval threshold can be defined by the user: if we drag the similarity button to the right, we track only the specific topic while if we drag it to the left, similar topics are retrieved as well.

Oslo Norway attack topic trend