Research for Global Development

Social Entrepreneurship Landscape on Twitter (#SocEnt analysis)


Terms

Links

Users

Users: Total Tweets

Users: Followers

Users: Following

 
Twitter has become a valuable source of public data to understand the patterns of communication among participants in certain communities. Depending on the level of community discussion, this can quickly lead to a large corpus of data. To glean actionable insights about volume and patterns of communication is no easy task. We approach this challenge in two steps. First, we iteratively identify and develop the right tools to collect, organize, and visualize the data (in this post, we use the Twitter API, Archivist Desktop, Python, Gephi and Google Charts API), and then secondly work with our partners, in this case InterMedia colleagues, to complement their desk research as part of an on-going study.

From September 14 to 16 we collected twitter status updates mentioning the hashtag #SocEnt (social entrepreneurship). Over three days we collected 3,753 tweets from 1,606 distinct users as part of the social entrepreneurship discussion on Twitter, where users circulated 2,887 links (1,817 distinct) to resources related to social entrepreneurs. Above is a dashboard with the top ten most shared links, most mentioned users, and top ten key terms (Hashtags) users mentioned in conjunction with #SocEnt.

We then looked at the interaction of participants in the discussion. Below you can find a network image of the #SocEnt discussion community. There are many ways to depict such a network. In this case, each color in the network represents a sub-community of users based on their frequent interaction with each other on the topic of social enterprise. The size of the node in each sub-community shows the mention frequency between users in the #SocEnt discussion community. This is based on how many times a user is mentioned by other users, which could indicate retweets or discussion. Finally, the weight of the edges between nodes indicates the velocity of interaction between these nodes in the network. For example: Aleevee8 seems to interact more with schwild, and Belongtoit seems to interact more with Schwabfound.

If we look at the network image above, we see that volume measures only tell part of the story. Exploring how prominent individuals in the network interact helps us better understand the underlying structure of the network and visually depict how certain user-produced-content spreads within the community.

 

 

InterMedia

Social Entrepreneurship Landscape on Twitter (#SocEnt analysis)


Terms

Links

Users

Users: Total Tweets

Users: Followers

Users: Following

 
Twitter has become a valuable source of public data to understand the patterns of communication among participants in certain communities. Depending on the level of community discussion, this can quickly lead to a large corpus of data. To glean actionable insights about volume and patterns of communication is no easy task. We approach this challenge in two steps. First, we iteratively identify and develop the right tools to collect, organize, and visualize the data (in this post, we use the Twitter API, Archivist Desktop, Python, Gephi and Google Charts API), and then secondly work with our partners, in this case InterMedia colleagues, to complement their desk research as part of an on-going study.

From September 14 to 16 we collected twitter status updates mentioning the hashtag #SocEnt (social entrepreneurship). Over three days we collected 3,753 tweets from 1,606 distinct users as part of the social entrepreneurship discussion on Twitter, where users circulated 2,887 links (1,817 distinct) to resources related to social entrepreneurs. Above is a dashboard with the top ten most shared links, most mentioned users, and top ten key terms (Hashtags) users mentioned in conjunction with #SocEnt.

We then looked at the interaction of participants in the discussion. Below you can find a network image of the #SocEnt discussion community. There are many ways to depict such a network. In this case, each color in the network represents a sub-community of users based on their frequent interaction with each other on the topic of social enterprise. The size of the node in each sub-community shows the mention frequency between users in the #SocEnt discussion community. This is based on how many times a user is mentioned by other users, which could indicate retweets or discussion. Finally, the weight of the edges between nodes indicates the velocity of interaction between these nodes in the network. For example: Aleevee8 seems to interact more with schwild, and Belongtoit seems to interact more with Schwabfound.

If we look at the network image above, we see that volume measures only tell part of the story. Exploring how prominent individuals in the network interact helps us better understand the underlying structure of the network and visually depict how certain user-produced-content spreads within the community.

 

 

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