Detecting trends in twitter time series

Bie, Tijl De; Lijffijt, Jefrey; Mesnage, Cédric; Santos-Rodríguez, Raúl

doi:10.1109/mlsp.2016.7738815

Cited by 10 publications

(8 citation statements)

References 7 publications

(8 reference statements)

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“…All studies that are known to us (e.g., see the papers [32][33][34][35][36][37][38][39]), which are devoted to the SOC detection on the social networks, are based on the spectral analysis of the time series in order to detect 1/f noise in them. In such studies, 1/f noise is the only SOC identifier.…”

Section: Discussionmentioning

confidence: 99%

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Identification of Self‐Organized Critical State on Twitter Based on the Retweets’ Time Series Analysis

Dmitriev

2021

Complexity

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There is a number of studies, in which it is established that the observed flows of microposts generated by microblogging social networks (e.g., Twitter) are characterized by avalanche-like behavior. Time series of microposts depicting such streams are the time series with a power-law distribution, with 1/f noise and long memory. Despite this, there are no studies devoted to the detection and analysis of self-organized critical state, subcritical phase, and supercritical phase. The presented paper is devoted to the detection and investigation of such critical states and phases. An algorithm is proposed that allowed to detect of critical phases and critical conditions on Twitter, based on the analysis of retweets time series corresponding to the three debates of the 2016 United States Presidential Election, as the most popular debate in the history of America, collecting 84 million live views.

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Section: Discussionmentioning

confidence: 99%

“…e emergence of the SOC on the social networks is evidenced by the avalanche-like dynamics of microposts observed in them [32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Identification of Self‐Organized Critical State on Twitter Based on the Retweets’ Time Series Analysis

Dmitriev

2021

Complexity

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“…The main results of this research were obtained by analyzing a single time series of microposts whose values however constitute a representative sample. Similar results of analysis of an empirical time series of a microblogging network are presented in [24][25][26][27][28][29]. We cannot claim that the time series samples studied by us or other researchers are representative, which would be essential for a generalization of the results onto the entire general population.…”

Section: Discussionmentioning

confidence: 73%

“…The purpose of the research is a nonlinear dynamical interpretation of the complexity of a microblogging network and the development of an appropriate network model that could explain its complexity using the third paradigm of nonlinear science called the complexity paradigm. Another motivation for the research was the results presented in [26][27][28][29][30][31] where the time series of a number of microposts are characterized by the majority of key signs of the system complexity (a detailed description of the key signs of the system complexity is presented in Section 2).…”

Section: Introductionmentioning

confidence: 99%

Complexity of a Microblogging Social Network in the Framework of Modern Nonlinear Science

2018

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Recent developments in nonlinear science have caused the formation of a new paradigm called the paradigm of complexity. The self-organized criticality theory constitutes the foundation of this paradigm. To estimate the complexity of a microblogging social network, we used one of the conceptual schemes of the paradigm, namely, the system of key signs of complexity of the external manifestations of the system irrespective of its internal structure. Our research revealed all the key signs of complexity of the time series of a number of microposts. We offer a new model of a microblogging social network as a nonlinear random dynamical system with additive noise in three-dimensional phase space. Implementations of this model in the adiabatic approximation possess all the key signs of complexity, making the model a reasonable evolutionary model for a microblogging social network. The use of adiabatic approximation allows us to model a microblogging social network as a nonlinear random dynamical system with multiplicative noise with the power-law in one-dimensional phase space.

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“…Tijl De Bie et al [9] proposed a probabilistic model that models the time series accurately which shows the results in terms of peaks on top over an exponential graph. They showed the number of tweets addressed per day to a particular twitter user.…”

Section: Literature Surveymentioning

confidence: 99%

Twitter data analysis: temporal and term frequency analysis with real-time event

Yadav

Joshi

Sasikala

2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. .From the past few years, World Wide Web (www) has become a prominent and huge source for user generated content and opinionative data. Among various social media, Twitter gained popularity as it offers a fast and effective way of sharing users' perspective towards various critical and other issues in different domain. As the data is hugely generated on cloud, it has opened doors for the researchers in the field of data science and analysis. There are various domains such as 'Political' domain, 'Entertainment' domain and 'Business' domain. Also there are various APIs that Twitter provides for developers 1) Search API, focus on the old tweets 2) Rest API, focuses on user details and allow to collect the user profile, friends and followers 3) Streaming API, which collects details like tweets, hashtags, geo locations. In our work we are accessing Streaming API in order to fetch real-time tweets for the dynamic happening event. For this we are focusing on 'Entertainment' domain especially 'Sports' as IPL-T20 is currently the trending on-going event. We are collecting these numerous amounts of tweets and storing them in MongoDB database where the tweets are stored in JSON document format. On this document we are performing time-series analysis and term frequency analysis using different techniques such as filtering, information extraction for textmining that fulfils our objective of finding interesting moments for temporal data in the event and finding the ranking among the players or the teams based on popularity which helps people in understanding key influencers on the social media platform.

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Detecting trends in twitter time series

Cited by 10 publications

References 7 publications

Identification of Self‐Organized Critical State on Twitter Based on the Retweets’ Time Series Analysis

Identification of Self‐Organized Critical State on Twitter Based on the Retweets’ Time Series Analysis

Complexity of a Microblogging Social Network in the Framework of Modern Nonlinear Science

Twitter data analysis: temporal and term frequency analysis with real-time event

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