Data Analytics Applications for Streaming Data From Social Media: What to Predict?

Frank Emmert-Streib; Olli P. Yli-Harja; Matthias Dehmer

doi:10.3389/fdata.2018.00002

Data Analytics Applications for Streaming Data From Social Media: What to Predict?

Frank Emmert-Streib^*
, Olli P. Yli-Harja
, Matthias Dehmer

^*Corresponding author for this work

Tampere University
Institute of Biosciences and Medical Technology
Institute for Systems Biology
UMIT-The Health and Life Sciences University
Nankai University

Research output: Contribution to journal › Review article › peer-review

Abstract

Social media in general provide great opportunities for mining massive amounts of text, image, and video-based data. However, what questions can be addressed from analyzing such data? In this review, we are focusing on microblogging services and discuss applications of streaming data from the scientific literature. We will focus on text-based approaches because they represent by far the largest cohort of studies and we present a taxonomy of studied problems.

Original language	English
Article number	2
Journal	Frontiers in Big Data
Volume	1
DOIs	https://doi.org/10.3389/fdata.2018.00002
Publication status	Published - 11 Sept 2018
Externally published	Yes

Keywords

big data
computational social science
data analytics
data science
forecasting
prediction model
scientometrics
social media

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10.3389/fdata.2018.00002

Cite this

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title = "Data Analytics Applications for Streaming Data From Social Media: What to Predict?",

abstract = "Social media in general provide great opportunities for mining massive amounts of text, image, and video-based data. However, what questions can be addressed from analyzing such data? In this review, we are focusing on microblogging services and discuss applications of streaming data from the scientific literature. We will focus on text-based approaches because they represent by far the largest cohort of studies and we present a taxonomy of studied problems.",

keywords = "big data, computational social science, data analytics, data science, forecasting, prediction model, scientometrics, social media",

author = "Frank Emmert-Streib and Yli-Harja, \{Olli P.\} and Matthias Dehmer",

note = "Publisher Copyright: Copyright {\textcopyright} 2018 Emmert-Streib, Yli-Harja and Dehmer.",

year = "2018",

month = sep,

day = "11",

doi = "10.3389/fdata.2018.00002",

language = "English",

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T1 - Data Analytics Applications for Streaming Data From Social Media

T2 - What to Predict?

AU - Emmert-Streib, Frank

AU - Yli-Harja, Olli P.

AU - Dehmer, Matthias

PY - 2018/9/11

Y1 - 2018/9/11

N2 - Social media in general provide great opportunities for mining massive amounts of text, image, and video-based data. However, what questions can be addressed from analyzing such data? In this review, we are focusing on microblogging services and discuss applications of streaming data from the scientific literature. We will focus on text-based approaches because they represent by far the largest cohort of studies and we present a taxonomy of studied problems.

AB - Social media in general provide great opportunities for mining massive amounts of text, image, and video-based data. However, what questions can be addressed from analyzing such data? In this review, we are focusing on microblogging services and discuss applications of streaming data from the scientific literature. We will focus on text-based approaches because they represent by far the largest cohort of studies and we present a taxonomy of studied problems.

KW - big data

KW - computational social science

KW - data analytics

KW - data science

KW - forecasting

KW - prediction model

KW - scientometrics

KW - social media

UR - https://www.scopus.com/pages/publications/85065914713

U2 - 10.3389/fdata.2018.00002

DO - 10.3389/fdata.2018.00002

M3 - Review article

AN - SCOPUS:85065914713

SN - 2624-909X

VL - 1

JO - Frontiers in Big Data

JF - Frontiers in Big Data

M1 - 2

ER -

Data Analytics Applications for Streaming Data From Social Media: What to Predict?

Abstract

Keywords

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