English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Social media such as Instagram and Twitter have become important platformsfor marketing and selling illicit drugs. Detection of online illicit drugtrafficking has become critical to combat the online trade of illicit drugs.However, the legal status often varies spatially and temporally; even for thesame drug, federal and state legislation can have different regulations aboutits legality. Meanwhile, more drug trafficking events are disguised as a novelform of advertising commenting leading to information heterogeneity.Accordingly, accurate detection of illicit drug trafficking events (IDTEs) fromsocial media has become even more challenging. In this work, we conduct thefirst systematic study on fine-grained detection of IDTEs on Instagram. Wepropose to take a deep multimodal multilabel learning (DMML) approach to detectIDTEs and demonstrate its effectiveness on a newly constructed dataset calledmultimodal IDTE(MM-IDTE). Specifically, our model takes text and image data asthe input and combines multimodal information to predict multiple labels ofillicit drugs. Inspired by the success of BERT, we have developed aself-supervised multimodal bidirectional transformer by jointly fine-tuningpretrained text and image encoders. We have constructed a large-scale datasetMM-IDTE with manually annotated multiple drug labels to support fine-graineddetection of illicit drugs. Extensive experimental results on the MM-IDTEdataset show that the proposed DMML methodology can accurately detect IDTEseven in the presence of special characters and style changes attempting toevade detection.

Detection of Illicit Drug Trafficking Events on Instagram: A Deep Multimodal Multilabel Learning Approach