Statistics of co-occurring keywords in confined text messages on Twitter View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2014-09

AUTHORS

J. Mathiesen, L. Angheluta, M. H. Jensen

ABSTRACT

Online social media such as the micro-blogging site Twitter has become a rich source of real-time data on online human behaviors. Here we analyze the occurrence and co-occurrence frequency of keywords in user posts on Twitter. From the occurrence rate of major international brand names, we provide examples of predictions of brand-user behaviors. From the co-occurrence rates, we further analyze the user-perceived relationships between international brand names and construct the corresponding relationship networks. In general the user activity on Twitter is highly intermittent and we show that the occurrence rate of brand names forms a highly correlated time signal. More... »

PAGES

1849-1858

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1140/epjst/e2014-02230-y

DOI

http://dx.doi.org/10.1140/epjst/e2014-02230-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1022297837


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Copenhagen", 
          "id": "https://www.grid.ac/institutes/grid.5254.6", 
          "name": [
            "Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mathiesen", 
        "givenName": "J.", 
        "id": "sg:person.01031653041.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01031653041.19"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Physics of Geological Processes, Department of Physics, University of Oslo, PO 1048, 0316, Blindern, Oslo, Norway"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Angheluta", 
        "givenName": "L.", 
        "id": "sg:person.0674666513.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0674666513.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Copenhagen", 
          "id": "https://www.grid.ac/institutes/grid.5254.6", 
          "name": [
            "Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jensen", 
        "givenName": "M. H.", 
        "id": "sg:person.01152213267.83", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01152213267.83"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1103/physreve.78.026123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000413713"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.78.026123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000413713"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1088/1742-5468/2006/11/l11001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002744668"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/j.1475-4932.2012.00809.x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013831750"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1207/s15516709cog2901_3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013933522"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jocs.2010.12.007", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015168619"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0706851105", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017158516"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35035023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017951524", 
          "https://doi.org/10.1038/35035023"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35035023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017951524", 
          "https://doi.org/10.1038/35035023"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1145/1772690.1772751", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024478168"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.0901136106", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027740372"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.97.168001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030288944"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.97.168001", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030288944"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.109.168701", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031281569"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.109.168701", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031281569"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.73.036127", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032715074"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physreve.73.036127", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032715074"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature03459", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041161194", 
          "https://doi.org/10.1038/nature03459"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature03459", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041161194", 
          "https://doi.org/10.1038/nature03459"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.105.158701", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042473307"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1103/physrevlett.105.158701", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042473307"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1073/pnas.1304179110", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045875686"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.sbspro.2011.10.562", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049073551"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014-09", 
    "datePublishedReg": "2014-09-01", 
    "description": "Online social media such as the micro-blogging site Twitter has become a rich source of real-time data on online human behaviors. Here we analyze the occurrence and co-occurrence frequency of keywords in user posts on Twitter. From the occurrence rate of major international brand names, we provide examples of predictions of brand-user behaviors. From the co-occurrence rates, we further analyze the user-perceived relationships between international brand names and construct the corresponding relationship networks. In general the user activity on Twitter is highly intermittent and we show that the occurrence rate of brand names forms a highly correlated time signal.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1140/epjst/e2014-02230-y", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1297403", 
        "issn": [
          "1951-6355", 
          "1951-6401"
        ], 
        "name": "The European Physical Journal Special Topics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "9", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "223"
      }
    ], 
    "name": "Statistics of co-occurring keywords in confined text messages on Twitter", 
    "pagination": "1849-1858", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "6a3b0c862194f84a15a54dedea72e2e7e3f4d9b2a0026d6cb4fc57fd64d34300"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1140/epjst/e2014-02230-y"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1022297837"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1140/epjst/e2014-02230-y", 
      "https://app.dimensions.ai/details/publication/pub.1022297837"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T20:51", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8684_00000536.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1140%2Fepjst%2Fe2014-02230-y"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1140/epjst/e2014-02230-y'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1140/epjst/e2014-02230-y'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1140/epjst/e2014-02230-y'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1140/epjst/e2014-02230-y'


 

This table displays all metadata directly associated to this object as RDF triples.

127 TRIPLES      21 PREDICATES      43 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1140/epjst/e2014-02230-y schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N83a2048ec33140b499fa5546a220f707
4 schema:citation sg:pub.10.1038/35035023
5 sg:pub.10.1038/nature03459
6 https://doi.org/10.1016/j.jocs.2010.12.007
7 https://doi.org/10.1016/j.sbspro.2011.10.562
8 https://doi.org/10.1073/pnas.0706851105
9 https://doi.org/10.1073/pnas.0901136106
10 https://doi.org/10.1073/pnas.1304179110
11 https://doi.org/10.1088/1742-5468/2006/11/l11001
12 https://doi.org/10.1103/physreve.73.036127
13 https://doi.org/10.1103/physreve.78.026123
14 https://doi.org/10.1103/physrevlett.105.158701
15 https://doi.org/10.1103/physrevlett.109.168701
16 https://doi.org/10.1103/physrevlett.97.168001
17 https://doi.org/10.1111/j.1475-4932.2012.00809.x
18 https://doi.org/10.1145/1772690.1772751
19 https://doi.org/10.1207/s15516709cog2901_3
20 schema:datePublished 2014-09
21 schema:datePublishedReg 2014-09-01
22 schema:description Online social media such as the micro-blogging site Twitter has become a rich source of real-time data on online human behaviors. Here we analyze the occurrence and co-occurrence frequency of keywords in user posts on Twitter. From the occurrence rate of major international brand names, we provide examples of predictions of brand-user behaviors. From the co-occurrence rates, we further analyze the user-perceived relationships between international brand names and construct the corresponding relationship networks. In general the user activity on Twitter is highly intermittent and we show that the occurrence rate of brand names forms a highly correlated time signal.
23 schema:genre research_article
24 schema:inLanguage en
25 schema:isAccessibleForFree false
26 schema:isPartOf Nda54a3cfe9fe47d2be422a9478d2d8eb
27 Nfcd499335d684c2594e7b10dfd9895e0
28 sg:journal.1297403
29 schema:name Statistics of co-occurring keywords in confined text messages on Twitter
30 schema:pagination 1849-1858
31 schema:productId N4c346ec2a06640c0989dc4ddbadd5680
32 N4d205619f98f434bb1d4e3025d1aea3a
33 Ne852bdd3dc87403e905aa9317bb55739
34 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022297837
35 https://doi.org/10.1140/epjst/e2014-02230-y
36 schema:sdDatePublished 2019-04-10T20:51
37 schema:sdLicense https://scigraph.springernature.com/explorer/license/
38 schema:sdPublisher N67fd4fc42f754877ad9d094db034e7b2
39 schema:url http://link.springer.com/10.1140%2Fepjst%2Fe2014-02230-y
40 sgo:license sg:explorer/license/
41 sgo:sdDataset articles
42 rdf:type schema:ScholarlyArticle
43 N231d057aa8704ffd90908f10b0e30e1e schema:name Physics of Geological Processes, Department of Physics, University of Oslo, PO 1048, 0316, Blindern, Oslo, Norway
44 rdf:type schema:Organization
45 N4c346ec2a06640c0989dc4ddbadd5680 schema:name dimensions_id
46 schema:value pub.1022297837
47 rdf:type schema:PropertyValue
48 N4d205619f98f434bb1d4e3025d1aea3a schema:name doi
49 schema:value 10.1140/epjst/e2014-02230-y
50 rdf:type schema:PropertyValue
51 N5ae2cc602ba246b5848cf2b29b907cf6 rdf:first sg:person.0674666513.17
52 rdf:rest Nd58ebd12df904886a5f7f14ed01af83f
53 N67fd4fc42f754877ad9d094db034e7b2 schema:name Springer Nature - SN SciGraph project
54 rdf:type schema:Organization
55 N83a2048ec33140b499fa5546a220f707 rdf:first sg:person.01031653041.19
56 rdf:rest N5ae2cc602ba246b5848cf2b29b907cf6
57 Nd58ebd12df904886a5f7f14ed01af83f rdf:first sg:person.01152213267.83
58 rdf:rest rdf:nil
59 Nda54a3cfe9fe47d2be422a9478d2d8eb schema:issueNumber 9
60 rdf:type schema:PublicationIssue
61 Ne852bdd3dc87403e905aa9317bb55739 schema:name readcube_id
62 schema:value 6a3b0c862194f84a15a54dedea72e2e7e3f4d9b2a0026d6cb4fc57fd64d34300
63 rdf:type schema:PropertyValue
64 Nfcd499335d684c2594e7b10dfd9895e0 schema:volumeNumber 223
65 rdf:type schema:PublicationVolume
66 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
67 schema:name Information and Computing Sciences
68 rdf:type schema:DefinedTerm
69 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
70 schema:name Artificial Intelligence and Image Processing
71 rdf:type schema:DefinedTerm
72 sg:journal.1297403 schema:issn 1951-6355
73 1951-6401
74 schema:name The European Physical Journal Special Topics
75 rdf:type schema:Periodical
76 sg:person.01031653041.19 schema:affiliation https://www.grid.ac/institutes/grid.5254.6
77 schema:familyName Mathiesen
78 schema:givenName J.
79 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01031653041.19
80 rdf:type schema:Person
81 sg:person.01152213267.83 schema:affiliation https://www.grid.ac/institutes/grid.5254.6
82 schema:familyName Jensen
83 schema:givenName M. H.
84 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01152213267.83
85 rdf:type schema:Person
86 sg:person.0674666513.17 schema:affiliation N231d057aa8704ffd90908f10b0e30e1e
87 schema:familyName Angheluta
88 schema:givenName L.
89 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0674666513.17
90 rdf:type schema:Person
91 sg:pub.10.1038/35035023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017951524
92 https://doi.org/10.1038/35035023
93 rdf:type schema:CreativeWork
94 sg:pub.10.1038/nature03459 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041161194
95 https://doi.org/10.1038/nature03459
96 rdf:type schema:CreativeWork
97 https://doi.org/10.1016/j.jocs.2010.12.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015168619
98 rdf:type schema:CreativeWork
99 https://doi.org/10.1016/j.sbspro.2011.10.562 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049073551
100 rdf:type schema:CreativeWork
101 https://doi.org/10.1073/pnas.0706851105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017158516
102 rdf:type schema:CreativeWork
103 https://doi.org/10.1073/pnas.0901136106 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027740372
104 rdf:type schema:CreativeWork
105 https://doi.org/10.1073/pnas.1304179110 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045875686
106 rdf:type schema:CreativeWork
107 https://doi.org/10.1088/1742-5468/2006/11/l11001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002744668
108 rdf:type schema:CreativeWork
109 https://doi.org/10.1103/physreve.73.036127 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032715074
110 rdf:type schema:CreativeWork
111 https://doi.org/10.1103/physreve.78.026123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000413713
112 rdf:type schema:CreativeWork
113 https://doi.org/10.1103/physrevlett.105.158701 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042473307
114 rdf:type schema:CreativeWork
115 https://doi.org/10.1103/physrevlett.109.168701 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031281569
116 rdf:type schema:CreativeWork
117 https://doi.org/10.1103/physrevlett.97.168001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030288944
118 rdf:type schema:CreativeWork
119 https://doi.org/10.1111/j.1475-4932.2012.00809.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1013831750
120 rdf:type schema:CreativeWork
121 https://doi.org/10.1145/1772690.1772751 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024478168
122 rdf:type schema:CreativeWork
123 https://doi.org/10.1207/s15516709cog2901_3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013933522
124 rdf:type schema:CreativeWork
125 https://www.grid.ac/institutes/grid.5254.6 schema:alternateName University of Copenhagen
126 schema:name Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100, Copenhagen, Denmark
127 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...