Flow140: Tracing the Flow of Conversations Ramine Tinati – [email protected] – Web and Internet Science – University of Southampton Research Overview Social Networks such as Twi2er offer a pla5orm for individuals to create and share messages, establish ‘friendships’ between each other, and even become part of specific communiAes. Twi2er has enabled a range of important social acAvity to succeed, including idenAfying public health issues and more recently, as a pla5orm for social and poliAcal change. However, in spite of this, the volumes of messages that are transmi2ed per day make idenAfying valuable content from the back cha2er and ulAmately, influenAal individuals from spam, difficult. Flow140 enables exploraAon into the dynamics of conversaAons between users within a Twi2er network in realAme or using historic data. Problems with analyzing and visualizing such large amounts of informaAon are overcome a filtering soluAon based on characterisAcs that individuals exhibit within the network, and the flow of a conversaAon. The Classification of Twitter Users Flow140 was based upon the desire to be able to visualize a growing network of communicaAons within the Twi2er service. IniAal Project Aims: • Can we capture how valuable informaAon flows in a conversaAon? • Can we iden*fy specific characterisAcs of individuals? • Can we visualise it? Tracing the flow of informaAon based on specific criteria: • Where does the valuable informaAon originate from? • The Idea Starter • How does the informaAon flow in a conversaAon? • The Amplifier • How can valuable informaAon cross streams? • The Curator Demonstration: – Filtered vs. Unfiltered Retweet Conversation Stream Fig 1. Unfiltered Retweet Network (a) Retweet Network as of Nov 3 rd 2011 (b) Retweet Network as of Nov 6 rd 2011 (c) Retweet Network as of Nov 9 rd 2011 (c) 21:00 Nov 8 rd 2011 (d) 09:00 Nov 9 rd 2011 (e) 21:00 Nov 9 rd 2011 (a) 21:00 Nov 3 rd 2011 Fig 2. Filtered Retweet Network Figure 1 represents the retweet network for #nov9 as a ‘flat’ network, in its original, unfiltered state. Figures 1 ac are visualizaAons of the network of individuals (the nodes) and the retweets (the edges) showing the dynamic growth of the network. This unfiltered state provides very li2le observaAonal informaAon or analyAcal detail. Even during the early stage of the conversaAons (Fig1.a) the idenAfying the flow of informaAon is difficult and the rapid growth of the communicaAons makes it difficult to idenAfy specific individuals and their roles. For example the network clusters that were idenAfiable before the protest on the 3 rd November become impossible to disAnguish by the 9 th November. Figure 2 shows how our tool’s filtering algorithm reduces the complexity of the network by concentraAng on the communicaAons between individuals exhibiAng certain network characterisAcs of interest (e.g. retweeAng) to handle these data at scale Figures 2 ad represent the growth of the same network as Figure 1, but this Ame with the filter applied, providing a clearer view of the structure and growth of the network. First observaAons of the network idenAfy a number of users whose presence was constant throughout the growth of the conversaAons. These users, represented by the red nodes, are individual that iniAated the conversaAons that led to the flow of informaAon; the node size is determined by the number of Ames they were iniAators.

Flow 140 by Ramine Tinati

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Social Networks such as Twitter offer a platform for individuals to create and share messages, establish ‘friendships’ between each other, and even become part of specific communities.Twitter has enabled a range of important social activity to succeed, including identifying public health issues and more recently, as a platform for social and political change. However, in spite of this, the volumes of messages that are transmitted per day make identifying valuable content from the back chatter and ultimately, influential individuals from spam, difficult.Flow140 enables exploration into the dynamics of conversations between users within a Twitter network in real-time or using historic data. Problems with analyzing and visualizing such large amounts of information are overcome a filtering solution based on characteristics that individuals exhibit within the network, and the flow of a conversation.

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Flow140: Tracing the Flow of Conversations Ramine Tinati – [email protected] – Web and Internet Science – University of Southampton

Research Overview

Social Networks such as Twi2er offer a pla5orm for individuals to create and share messages, establish ‘friendships’ between each other, and even become part of specific communiAes. Twi2er has enabled a range of important social acAvity to succeed, including idenAfying public health issues and more recently, as a pla5orm for social and poliAcal change. However, in spite of this, the volumes of messages that are transmi2ed per day make idenAfying valuable content from the back cha2er and ulAmately, influenAal individuals from spam, difficult. Flow140 enables exploraAon into the dynamics of conversaAons between users within a Twi2er network in real-‐Ame or using historic data. Problems with analyzing and visualizing such large amounts of informaAon are overcome a filtering soluAon based on characterisAcs that individuals exhibit within the network, and the flow of a conversaAon.

The Classification of Twitter Users

Flow140 was based upon the desire to be able to visualize a growing network of communicaAons within the Twi2er service. IniAal Project Aims: •  Can we capture how valuable informaAon flows in a

conversaAon? •  Can we iden*fy specific characterisAcs of individuals? •  Can we visualise it? Tracing the flow of informaAon based on specific criteria: •  Where does the valuable informaAon originate from?

•  The Idea Starter

•  How does the informaAon flow in a conversaAon? •  The Amplifier

•  How can valuable informaAon cross streams? •  The Curator

Demonstration: – Filtered vs. Unfiltered Retweet Conversation Stream

Fig 1. Unfiltered Retweet Network

(a) Retweet Network as of Nov 3rd 2011

(b) Retweet Network as of Nov 6rd 2011

(d) 09:00 Nov 9rd 2011

(e) 21:00 Nov 9rd 2011

(a) 21:00 Nov 3rd 2011

Fig 2. Filtered Retweet Network

Figure 1 represents the retweet network for #nov9 as a ‘flat’ network, in its original, unfiltered state. Figures 1 a-‐c are visualizaAons of the network of individuals (the nodes) and the retweets (the edges) showing the dynamic growth of the network. This unfiltered state provides very li2le observaAonal informaAon or analyAcal detail. Even during the early stage of the conversaAons (Fig1.a) the idenAfying the flow of informaAon is difficult and the rapid growth of the communicaAons makes it difficult to idenAfy specific individuals and their roles. For example the network clusters that were idenAfiable before the protest on the 3rd November become impossible to disAnguish by the 9th November.

Figure 2 shows how our tool’s filtering algorithm reduces the complexity of the network by concentraAng on the communicaAons between individuals exhibiAng certain network characterisAcs of interest (e.g. retweeAng) to handle these data at scale Figures 2 a-‐d represent the growth of the same network as Figure 1, but this Ame with the filter applied, providing a clearer view of the structure and growth of the network. First observaAons of the network idenAfy a number of users whose presence was constant throughout the growth of the conversaAons. These users, represented by the red nodes, are individual that iniAated the conversaAons that led to the flow of informaAon; the node size is determined by the number of Ames they were iniAators.