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Contraction of online response to major events.

Szell M, Grauwin S, Ratti C - PLoS ONE (2014)

Bottom Line: We show that on the one hand this effect can be observed in the behavior of most regular users, and on the other hand is accentuated by the engagement of additional user demographics who only post during phases of high collective activity.Our measurements have practical implications for the design of micro-blogging and messaging services.In the case of the existing service Twitter, we show that the imposed limit of 140 characters per message currently leads to a substantial fraction of possibly dissatisfying to compose tweets that need to be truncated by their users.

View Article: PubMed Central - PubMed

Affiliation: Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

ABSTRACT
Quantifying regularities in behavioral dynamics is of crucial interest for understanding collective social events such as panics or political revolutions. With the widespread use of digital communication media it has become possible to study massive data streams of user-created content in which individuals express their sentiments, often towards a specific topic. Here we investigate messages from various online media created in response to major, collectively followed events such as sport tournaments, presidential elections, or a large snow storm. We relate content length and message rate, and find a systematic correlation during events which can be described by a power law relation--the higher the excitation, the shorter the messages. We show that on the one hand this effect can be observed in the behavior of most regular users, and on the other hand is accentuated by the engagement of additional user demographics who only post during phases of high collective activity. Further, we identify the distributions of content lengths as lognormals in line with statistical linguistics, and suggest a phenomenological law for the systematic dependence of the message rate to the lognormal mean parameter. Our measurements have practical implications for the design of micro-blogging and messaging services. In the case of the existing service Twitter, we show that the imposed limit of 140 characters per message currently leads to a substantial fraction of possibly dissatisfying to compose tweets that need to be truncated by their users.

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Lognormal distribution of message lengths and dependence of its parameters on excitation.(A) Probability distributions of content length  of messages gathered by logarithmically binned classes of different hourly volume  (circles), and corresponding lognormal fits (dashed curves, fit ranges 0 to 120). During low-volume phases (pink and blue), the distribution grows slowly. For high-volume phases (orange and red) however the distribution grows fast and peaks at . Peaks at the maximum length of 140 are an artifact from the length limitation in the specific medium (Twitter), absent for unlimited media, see Section S2 in File S1. For visual clarity only every third data point is shown. (B) Plot of the lognormal fit parameter  against message rate  demonstrates the systematic relation between message rate and length, dashed line. (C) Plot of the lognormal fit parameter  versus message rate . Here the value of  increases with the message rate  to some point and appears independent of the volume class in high volume regimes. Error bars denote  confidence intervals.
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pone-0089052-g002: Lognormal distribution of message lengths and dependence of its parameters on excitation.(A) Probability distributions of content length of messages gathered by logarithmically binned classes of different hourly volume (circles), and corresponding lognormal fits (dashed curves, fit ranges 0 to 120). During low-volume phases (pink and blue), the distribution grows slowly. For high-volume phases (orange and red) however the distribution grows fast and peaks at . Peaks at the maximum length of 140 are an artifact from the length limitation in the specific medium (Twitter), absent for unlimited media, see Section S2 in File S1. For visual clarity only every third data point is shown. (B) Plot of the lognormal fit parameter against message rate demonstrates the systematic relation between message rate and length, dashed line. (C) Plot of the lognormal fit parameter versus message rate . Here the value of increases with the message rate to some point and appears independent of the volume class in high volume regimes. Error bars denote confidence intervals.

Mentions: Dividing the probability distribution of content length into logarithmically binned classes of different volumes reveals a smooth transition between the low-volume and high-volume phases showing a wide variety of states, Fig. 2A. In times of low message rates (purple and blue circles), the distribution grows slowly until , stays approximately constant until and peaks shortly before the maximum length of . During high-volume phases however (red and orange circles) the distribution grows fast, peaks around , then decreases to a low local minimum around and displays a smaller peak again at . Distributions of message lengths in other data sets follow similar shapes. The second peak however is an artifact introduced by message length limitation, therefore it only appears in Twitter and in data set 5.


Contraction of online response to major events.

Szell M, Grauwin S, Ratti C - PLoS ONE (2014)

Lognormal distribution of message lengths and dependence of its parameters on excitation.(A) Probability distributions of content length  of messages gathered by logarithmically binned classes of different hourly volume  (circles), and corresponding lognormal fits (dashed curves, fit ranges 0 to 120). During low-volume phases (pink and blue), the distribution grows slowly. For high-volume phases (orange and red) however the distribution grows fast and peaks at . Peaks at the maximum length of 140 are an artifact from the length limitation in the specific medium (Twitter), absent for unlimited media, see Section S2 in File S1. For visual clarity only every third data point is shown. (B) Plot of the lognormal fit parameter  against message rate  demonstrates the systematic relation between message rate and length, dashed line. (C) Plot of the lognormal fit parameter  versus message rate . Here the value of  increases with the message rate  to some point and appears independent of the volume class in high volume regimes. Error bars denote  confidence intervals.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3935844&req=5

pone-0089052-g002: Lognormal distribution of message lengths and dependence of its parameters on excitation.(A) Probability distributions of content length of messages gathered by logarithmically binned classes of different hourly volume (circles), and corresponding lognormal fits (dashed curves, fit ranges 0 to 120). During low-volume phases (pink and blue), the distribution grows slowly. For high-volume phases (orange and red) however the distribution grows fast and peaks at . Peaks at the maximum length of 140 are an artifact from the length limitation in the specific medium (Twitter), absent for unlimited media, see Section S2 in File S1. For visual clarity only every third data point is shown. (B) Plot of the lognormal fit parameter against message rate demonstrates the systematic relation between message rate and length, dashed line. (C) Plot of the lognormal fit parameter versus message rate . Here the value of increases with the message rate to some point and appears independent of the volume class in high volume regimes. Error bars denote confidence intervals.
Mentions: Dividing the probability distribution of content length into logarithmically binned classes of different volumes reveals a smooth transition between the low-volume and high-volume phases showing a wide variety of states, Fig. 2A. In times of low message rates (purple and blue circles), the distribution grows slowly until , stays approximately constant until and peaks shortly before the maximum length of . During high-volume phases however (red and orange circles) the distribution grows fast, peaks around , then decreases to a low local minimum around and displays a smaller peak again at . Distributions of message lengths in other data sets follow similar shapes. The second peak however is an artifact introduced by message length limitation, therefore it only appears in Twitter and in data set 5.

Bottom Line: We show that on the one hand this effect can be observed in the behavior of most regular users, and on the other hand is accentuated by the engagement of additional user demographics who only post during phases of high collective activity.Our measurements have practical implications for the design of micro-blogging and messaging services.In the case of the existing service Twitter, we show that the imposed limit of 140 characters per message currently leads to a substantial fraction of possibly dissatisfying to compose tweets that need to be truncated by their users.

View Article: PubMed Central - PubMed

Affiliation: Senseable City Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.

ABSTRACT
Quantifying regularities in behavioral dynamics is of crucial interest for understanding collective social events such as panics or political revolutions. With the widespread use of digital communication media it has become possible to study massive data streams of user-created content in which individuals express their sentiments, often towards a specific topic. Here we investigate messages from various online media created in response to major, collectively followed events such as sport tournaments, presidential elections, or a large snow storm. We relate content length and message rate, and find a systematic correlation during events which can be described by a power law relation--the higher the excitation, the shorter the messages. We show that on the one hand this effect can be observed in the behavior of most regular users, and on the other hand is accentuated by the engagement of additional user demographics who only post during phases of high collective activity. Further, we identify the distributions of content lengths as lognormals in line with statistical linguistics, and suggest a phenomenological law for the systematic dependence of the message rate to the lognormal mean parameter. Our measurements have practical implications for the design of micro-blogging and messaging services. In the case of the existing service Twitter, we show that the imposed limit of 140 characters per message currently leads to a substantial fraction of possibly dissatisfying to compose tweets that need to be truncated by their users.

Show MeSH
Related in: MedlinePlus