One Year of Presidency in Lebanon – A Twitter Discussion Analysis

The Sample

The following analysis is based on a sample of 11,919 tweets extracted using Twitter’s API. The search term for this sample is  #سنة_من_عمر_وطن  which is the  “one year from a country’s life” hashtag launched by the President’s movement(s). Twitter’s Search API is focused on relevance and not completeness. This means that some Tweets and users may be missing from search results. Unfortunately, it is not easy to determine the total number of tweets for this hashtag. However, we can roughly estimate this number to be between 20 to 30% of the total number of tweets for this hashtag.

 

Tweets have been grouped by cluster using the Clauset-Newman-Moore algorithm using NodeXL.

Almost Complete Absence of the March 14 Component

The most noticeable aspect of the discussion is the almost total absence of politicians of March 14. Saad Hariri was mentioned by some users but did not tweet himself (as least in our representative sample). Samir Geagea and Walid Joumblatt did not appear in the sample.

 

In fact, we double-checked Samir Geagea’s profile, and it looks like he never tweeted regarding the end of the first year of the presidency.

 

Only a very small group of people (9 users in our sample) attacked the regime and its relation with Hezbollah. Two other groups of 20 users (total) sarcastically commented on the President’s answers to the journalists.

 

The logical explanation for this behavior is the extreme polarization of the Lebanese society. In such cases of strong polarization, people from politically competing groups don’t use the same hashtags or join the discussion. This explains their almost total absence.

 

The Overwhelming Joy of Followers

The hashtag was launched by the pro-President movement. It is therefore logical to have an overwhelming presence of pro-President users tweeting and using the hashtag.

 

Pro-President users are not however a tight crowd, i.e. a close community. The largest group of tweeters is 493 users tweeting 2056 times. This group does not include a notable politician, not even the President.

 

A dismantled community

The fact that the largest pro-Aounists group of tweeters is leaderless could be interpreted as a symptom of leadership crisis.

 

This is also shown in the  way pro-President groups are divided:

  • A leaderless group (493 users, 2056 tweets)
  • A President-Bassil-Kanaan-Jamil el Sayyed group (96 users, 269 tweets)
  • The official account for the Presidency (77 users, 125 tweets)
  • An Alain Aoun group (57 users, 69 tweets)

While many bridges connect the Kanaan-Bassil-Sayyed group (Kanaan being the most retweeted) to the main group of fans, the connections between this group and the Alain Aoun group are almost non-existant (5 in total).

 

Part of the Pro-President group is discussing with the Kanaan group (224 incoming connections and 142 outgoing connections) while other users from this group are discussing with Alain Aoun (52 connections and 35 outgoing connections). An explanation would be that, while the President’s fans are all happy with the “successes” of the first year of presidency, they look divided in terms of affiliation.

Hezbollah’s Support

It is rare not to see Hezbollah’s fans join political discussions on Twitter. In the case of the presidency’s hashtag, we notice some very strong support from Hezbollah’s users with tweets about the alliance between President Aoun and Sayyed Hassan Nasrallah.

 

The Importance of the Role of Mr. Gebran Bassil

While the discussion was primarily centered around the presidency, it is important to mention that a discussion about the positive role (as a supporter of the Hezbollah) and another one about the negative role of Gebran Bassil (corruption) was taking place.

 

Even though only a few users discussed the role of Mr. Gebran Bassil, this shows that he is a major concern (positive or negative) to many citizen.

 

Suleiman Frangieh Supporters

Finally, the most important aspect of the debate is probably the fact that the second largest clique in the discussion is a group with several discussion “mayors”, the most important two being Suleiman Frangieh supporters, Sleiman Frangieh (note that this is a different Suleiman Frangieh – @avsl_frangieh) and Georges Bou Nassif (@georgesbnassif). These users challenge the so called “success” by asking “which country are you talking about?”

Independents and Journalists, like Mariam al Bassam (New TV) and Yazbeck Wehbé (LBC), are also part of the debate against the “happy ones”.

The absence or at least very small involvement of people from the Future Movement, the Lebanese Forces and the Kataeb is noticeable.

 

As a result, we suggest that the real skirmish today is between the President’s supporters and Mr. Suleiman Frangieh’s supporters, while other Christians and ex-14 March groups are taking a distant neutral and silent stance from the joy or the frustrations of the first year of Presidency.

 

AUB – OSB – Social Media Strategy Course 2017

Rita Hayek
Rita Hayek – OSB MBA
Michele Khalife – Total Liban
Michele Khalife – Total Liban
George Khalaf – YouTuber
George Khalaf – YouTuber
Rachad Saddi – Google
Rachad Saddi – Google

 

Nasri Messarra – OSB – AUB
Nasri Messarra – OSB – AUB

I would like to thank all my guests, Mr. Rachad Saddi (EMEA Channel Partner Manager, Global Partnerships at Google), Mrs. Michèle Khalifé (Head of Marketing and Communication at Total Liban),  Mrs. Rita Hayek (Actress), Mr. George Khalaf (YouTuber and Film Director) for joining in and sharing their experience with my students.

 

Special thanks to Marc Smith and Arber Ceni for their support with NodeXL and for making sure we have the best experience in mapping Twitter and Facebook discussions.

 

Finally, thanks to all my students for making this time memorable: Jinane Chamseddine, Maya Kabakibi, Nada Jahchan, Reel ElZein, Jad Makarem, Sara Mansour, John Tamer, Bassem Youssef, Wael Abi Jumaa, Ralph Adaimy, Muriel Fourcroy, Joelle Audi, Ghida ElBaba, Elisa Samaha, Sarah Bou Daher, Joseph Yazbeck Ramadi & Layal Hossary.

K2PCenter Training

Third training session about Twitter discussion analysis at the Knowledge to Policy (#K2Pcenter) Center of the American University of Beirut. Social Graph visualization using NodeXL. Thanks for all those who joined the #k2pworkshop discussion.

Targeting cities or regions in Lebanon with Facebook ads

Until recently, it was impossible to target organically regions or areas in Lebanon with Facebook ads. This feature is now available. In the screenshots below, I describe the new option and the one I was using and that I still use for political marketing (voters and users can use the Internet from work or home, and vote or live in a different area).

 

The new method

In the location are of your ad creator, you are now able to select cities or regions with a radius (how many miles around the city in all direction). In the example below, I chose “no radius” to limit the targeting to Achrafieh only without its surrounding areas:

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My own method

This is the method I was using before this option has been made available. It is very similar to a previous post about targeting the competition. The Idea is to find very specific pages about a region and target the fans of these pages using the “interests” option in the ad creator page. In the example below, I targeted the fans of “Achrafieh 2020” and “Achrafieh”:

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This article was mentioned by Catherine Stevens in http://paper.li/cas314159?edition_id=c2714300-ff1b-11e4-871e-0cc47a0d15fd

Simple Walkthrough to Visualizing Twitter Data on NodeXL

In this post, we will consider analyzing the Arabic hashtag 1 with NodeXL. If you’re searching for a Latin string or hashtag, go directly to step 3.

1- On some computers, searching for an non-Latin string may cause NodeXL to return a null result. The safest option is to convert the Arabic string to URL code. Several websites offer a conversion tool. Look for “URL encode decode” on Google. We will use the following website: http://meyerweb.com/eric/tools/dencoder/.

2- type the hashtag in the text box and press “encode” then copy the resulting code:1 2

3- Open NodeXL and select “import from Twitter search network”:

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4- Paste the code from step 2 in the search box. You may want to limit the number of tweets (in this example, we limited the number of tweets to 1,000). Note that you may need authorize NodeXL to use your twitter account if this is your first Twitter analysis.

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5- The worksheet is automatically filled after the search. In the vertices sheet, you can check the names of the tweeters and some useful information about their popularity (followers) that can be combined with other data in your analysis:

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6- Choose the “Harel-Koren” algorithm and “show graph”:

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In its current state, the graph doesn’t say much. Yet, it can give you an idea of the structure of the network and you can mouse-over the vertices to read the tweets and the mentions. We will improve the layout in the next steps.

7- In the NodeXL ribbon tab, click on “Graph Metrics”. Then, “Select All” and “Calculate Metrics”.

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8- The data compiled can be used to analyze the network and its characteristics (outside the scope of this article which is limited to retrieval and display). Note, that in the vertices sheet, the “in degree” represents the number of times the tweeter was mentioned and the “out degree” the number of times he mentioned someone else. 2

9- In this step, we will group the vertices into clusters. In the NodeXL tab on the ribbon, click on “groups”, “group by clusters” and put neighbourless in one group to avoid having all your singletons displayed as a stand alone group:

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Note the Groups and Group Vertices worksheets.

10- In the layout algorithm dropdown, select “layout options” and “layout groups…”

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11- The resulting graph show small independent clusters with no interaction between each other. Note the singletons in the first group (people who were never mentioned). Check the following article for details about twitter network structures: http://www.smrfoundation.org/2014/03/02/6-kinds-of-twitter-social-media-network-structures/

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12- To display images instead of dots, in the vertices sheet, select “image” in the shape column, go to the NodeXL tab in the ribbon, select group, group options and check “the shapes specified in the shape column….” to use the shapes defined in vertices sheet instead of those defined in the Group Vertices sheet (this also works for colors).

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Note the size and opacity options you can also use to improve the layout.

13- The Autofill option allows you to quickly fill a column to modify the layout of your graph (note that you need to refresh the graph to see the results). Try to change the shape, opacity, etc.

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In this screenshot, the size varies from 1.5 to 100 depending on the in-degree (number of times the user was mentioned):

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14- To ungroup the vertices, select layout options from the algorithm dropdown in the graph and select “layout entire graph” instead of group… (see step 10)

15- to save the graph as an image, right-click on it and select “save to image”

16- to share your work with others (optional), choose “export to NodeXL Gallery” from the NodeXL tab in the ribbon

Articles and videos to watch and read:

– About Twitter Network structures: http://www.smrfoundation.org/2014/03/02/6-kinds-of-twitter-social-media-network-structures/

– About social networks, mapping and measuring Connections: https://www.youtube.com/watch?v=b5RonanIOF8#t=26

– A walkthrough to using NodeXL to visualize twitter networks: https://www.youtube.com/watch?v=PC-PgkhpsNc

Banning a non-fan from a Facebook page

A Facebook troll is someone who posts aggressive comments on your page – often immediately after you publish a post –  in an attempt of creating a firestorm of controversy.

 

Most often, these trolls do not like the page and it becomes impossible to ban them or stop them using the straightforward ban user function provided by the Facebook interface.

 

Below is a method you can use to ban a non-fan:

 

1) Find the Facebook ID of the troll. To get the id, you need to use “open graph”: Type https://graph.facebook.com/username (where username is the username of the troll, e.g. https://graph.facebook.com/john.doe)

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2) Open your page and go to the settings section, “banned users” function and select “people who like this” to see the whole list of users:

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3) Click on F12 or Ctrl+Shift+C to inspect the page (depends on your browser. Works both with Firefox and Chrome).

In the code, locate any “remove” button and replace the ID of the user with the troll’s ID. Beware not to change the page_id (first ID in the URL)

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4) Now click on the “remove” button you edited. In this example, I used ID=4. You will need to check “Ban Permanently” to prevent the user to like, write or comment in the future:

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How to count your shares

Some social plugins give you a total number of shares without specifying exactly where those shares came from.

Also, you may need to find if someone shared your link on Facebook, Twitter and other social networking websites:

Count your shares on Facebook:

– http://graph.facebook.com/?id=YourURL, e.g. http://graph.facebook.com/?id=http://nasri.messarra.com/use-two-steps-authentication-with-google-easy-steps/

Count your shares on Twitter:

– http://cdn.api.twitter.com/1/urls/count.json?url=YourURL, e.g. http://cdn.api.twitter.com/1/urls/count.json?url=http://nasri.messarra.com/use-two-steps-authentication-with-google-easy-steps/

Count your shares on Pinterest:

- http://api.pinterest.com/v1/urls/count.json?url=YourURL, e.g. http://api.pinterest.com/v1/urls/count.json?url=http://nasri.messarra.com/use-two-steps-authentication-with-google-easy-steps/