What does @marshawright look like on Twitter?

Marsha Wright is a well distinguished business expert, an entrepreneur, and is easily one of the most successful Twitter users the platform hosts. Users may be familiar with the  initiative, created by Marsha, which is a trending movement which has many millions of shares, and has been featured as a trending topic on Twitter hundreds of times.

In this post we used NodeXL to generate a network graph and associated report based on the Twitter handle ‘@marshawright‘. The output can be found below in figure 1 and displays Twitter activity from the previous week or so.

Figure 1 – Network graph of ‘@Marshawright’


The visualization looks great, but you may be asking, what does this all mean?

The top left hand side of each group is labeled e.g. G1 refers to ‘Group 1’ and G2 refers to ‘Group 2’ and so forth, and the keywords relate to the most frequently occurring per each group. NodeXL network graph reports also produce a number of other metrics such as the most frequently shared URLs, Domains, Hashtags, Words, Word Pairs, Replied-To, Mentioned Users, and most frequent tweeters These metrics are produced overall and also by group of Twitter users. By looking at different metrics associated with different groups (G1, G2, G3 etc) you can see the different topics that users may be talking about.

We can also take a look at the 6 types of network structures from  Smith, Rainie, Shneiderman, & Himelboim (2014) as a guide in interpretation.

Figure 2 ‘Six Types of Network Structures’


As show in the figure above different topics on social media can have contrasting network patterns. For instance in the polarized crowd discussion one set of users may talk about Donald Trump and others about Hilary Clinton, in the unified crowd users may talk about different aspects of the election, and in brand clusters people may offer an opinion related to the election without being connected to one another and without mentioning each other.

In a community cluster a group of users may talk about the different news articles surrounding Hilary Clinton. Broadcast networks are typically found when analysing news accounts as these disseminate news which is retweeted by a large amount of users. We can think of support networks as those accounts which reply to a large number of accounts, we can think of the customer support of a bank which may reply to a large amount of Twitter users.

Now if we take a look at Marsha’s graph again:

Figure 1 (revisited) – Network graph of ‘@Marshawright’


We can see that G1 (Group 1) resembles a broadcast network where @Marshawright’s account is in the centre of the group and accounts around the group are those which are retweeting and/or engaging with Marsha’s account. This is quite an impressive broadcast network and resembles that of news media organisations. We can also see a number of smaller groups which also resemble a broadcast network, which may indicate that Marsha’s tweets tend to have a high level of share-ability.

The full report can be found here, and there are quite a lot of analytics produced alongside the network graph. In our blog posts we like to highlight different metrics that NodeXL can produce. In this post we take a look at words overall and the level of sentiment which is produced based on tweet content.

Top Words in Tweet in Entire Graph:

[4252] Words in Sentiment List#1: Positive

[1071] Words in Sentiment List#2: Negative

[27] Words in Sentiment List#3: Angry/Violent

[60120] Non-categorized Words

[65445] Total Words

[2991] thinkbigsundaywithmarsha

[1819] marshawright

[548] https

[432] quotes

[402] entrepreneur

The information above, not surprisingly, displays that the most frequently occurring word in Marsha’s network graph is ‘ thinkbigsundaywithmarsha’ (n=2911). This is a topic that is tweeted about by followers of the initiative on a weekly basis and which involves sharing positive quotes by Twitter users. The graph also displays the sentiment of the words in discussions that involve Marsha, and we can see here clearly, that discussions tend to be positive as there number of words in the positive sentiment list outnumber those in the negative sentiment list.

The graphs are topical and are likely to alter on a week by week basis, however, it is very likely to be the case that the keyword ‘thinkbigsundaywithmarsha‘ would appear in future NodeXL graphs and reports. To further highlight this point we can examine a time series graph created in Visibrain, which charts the Twitter activity of ‘thinkbigsundaywithmarsha‘, including keywords and hashtags.


We can see that every-weekend (when followers of Marsha are encouraged to tweet using the hashtag) there is a quite a large peak in tweets. Therefore, we can conclude that this is keyword which will be present in future network graphs and reports.

Here at Sonic Social Media we like Twitter influencers, and so we offer completely free of charge analysis of accounts which belong to those whom identify as Twitter influencers. You can complete this form and we can have something back to you within 48 hours.

Want to learn more? Check out this NodeXL and Social Network Analysis workshop at Leeds Becket University which will cover all of the methods used in this post in much depth.


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