SZodixStudyAcademic Library

Shelves Open access

Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated

Identifying the High-Value Social Audience from Twitter through Text-Mining Methods · Study

ChapterShelves

ChapterIdentifying the High-Value Social Audience from Twitter through Text-Mining Methods

Siaw Ling Lo2014

OA

Chaptergreen2014

Identifying the High-Value Social Audience from Twitter through Text-Mining Methods

Siaw Ling Lo · David J. Cornforth · Raymond Chiong

21 citations·0/0 files stored·Proceedings in adaptation, learning and optimization

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Computational and Text Analysis Methods Digital Marketing and Social Media Sentiment Analysis and Opinion Mining

Abstract

No abstract was published for this record.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 21 citations exist upstream.

References

2013Private traits and attributes are predictable from digital records of human behavior 2011Comparing Twitter and Traditional Media Using Topic Models 2010You are who you know

Cited by

2015The role of technology in the management and exploitation of internal business intelligence 2016A review on social audience identification on twitter using text mining methods

Related works

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

2014Online Social Networks: Threats and Solutionsarticle · 2 shared references 2018What demographic attributes do our digital footprints reveal? A systematic reviewreview · 2 shared references 2016Your Cart tells Youother · 2 shared references 2016Proceedings of the First Workshop on NLP and Computational Social Scienceother · 2 shared references 2018Understanding the predictability of user demographics from cyber-physical-social behaviours in indoor retail spacesarticle · 2 shared references 2016Multi-task Representation Learning for Demographic Predictionother · 2 shared references

Details

Year2014

Typechapter

Published2014-11-04

VenueProceedings in adaptation, learning and optimization

ISSN-L2363-6084

Languageen

Citations21

Files0 public / 0

ShelfComputational and Text Analysis Methods

Identifiers

doi10.1007/978-3-319-13359-1_26

openalex_workW159937948

Links

Imported from OpenAlex (CC0) — nothing here is generated.