Data · dataset · 2026
Exploring text representations for online misinformation
Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.17034/32634906.v1
Mis- and disinformation, commonly collectively called fake news, continue to menace society.
Description
Perhaps, the impact of this age-old problem is presently most plain in politics and healthcare. However, fake news is affecting an increasing number of domains.
It takes many different forms and continues to shapeshift as technology advances. Though it arguably most widely spreads in textual form, e.g., through social media posts and blog articles.<br><br>Thus, it is imperative to thwart the spread of textual misinformation, which necessitates its initial detection. This thesis contributes to the creation of representations that are useful for detecting misinformation.<br><br>Firstly, it develops a novel method for extracting textual features from news articles for misinformation detection.
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These features harness the disparity between the thematic coherence of authentic and false news stories. In other words, the composition of themes discussed in both groups significantly differs as the story progresses.<br><br>Secondly, it demonstrates the effectiveness of topic features for fake news detection, using classification and clustering. Clustering is particularly useful because it alleviates the need for a labelled dataset, which can be labour-intensive and time-consuming to amass.<br><br>More generally, it contributes towards a better understanding of misinformation and ways of detecting it using Machine Learning and Natural Language Processing.<br><br>
Links
Where it is published
- DOI doi.org/10.17034/32634906.v1 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Computer Science & AI · Computer Science & AI · Computer Science & AI · Earth & Environmental Science · Earth & Environmental Science · Earth & Environmental Science · Humanities · Humanities · Humanities · Machine learning · Machine learning · Machine learning · Natural language processing · Natural language processing · Natural language processing
- Inferred from text
- Text 75%
Provenance · 3 source records, 20 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/32634906 | 5 d ago | JSON v1 |
| Deakin Research Online | oai:figshare.com:article/32634906 | 5 d ago | JSON v1 |
| DMU Figshare | oai:figshare.com:article/32634906 | 5 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:field:460208 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['natural language processing'] |
| concepts[field].anzsrc:field:460208 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['natural language processing'] |
| concepts[field].anzsrc:field:460208 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['natural language processing'] |
| concepts[field].anzsrc:group:4611 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].local:field:computer-science-ai | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:humanities | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:humanities | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:humanities | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[modality].local:modality:text | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license_text | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |