BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//conferences.acspri.org.au//2024//speaker//LTP3D7 BEGIN:VTIMEZONE TZID:AEST BEGIN:STANDARD DTSTART:20000326T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3;UNTIL=20050326T170000Z TZNAME:AEST TZOFFSETFROM:+1100 TZOFFSETTO:+1000 END:STANDARD BEGIN:STANDARD DTSTART:20060402T040000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4;UNTIL=20060401T170000Z TZNAME:AEST TZOFFSETFROM:+1100 TZOFFSETTO:+1000 END:STANDARD BEGIN:STANDARD DTSTART:20070325T040000 RRULE:FREQ=YEARLY;BYDAY=4SU;BYMONTH=3;UNTIL=20070324T170000Z TZNAME:AEST TZOFFSETFROM:+1100 TZOFFSETTO:+1000 END:STANDARD BEGIN:STANDARD DTSTART:20080406T040000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=4 TZNAME:AEST TZOFFSETFROM:+1100 TZOFFSETTO:+1000 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000827T030000 RRULE:FREQ=YEARLY;BYDAY=4SU;BYMONTH=8;UNTIL=20000826T170000Z TZNAME:AEDT TZOFFSETFROM:+1000 TZOFFSETTO:+1100 END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20011028T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10;UNTIL=20071027T170000Z TZNAME:AEDT TZOFFSETFROM:+1000 TZOFFSETTO:+1100 END:DAYLIGHT BEGIN:DAYLIGHT DTSTART:20081005T030000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=10 TZNAME:AEDT TZOFFSETFROM:+1000 TZOFFSETTO:+1100 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-2024-8BSAUZ@conferences.acspri.org.au DTSTART;TZID=AEST:20241129T093000 DTEND;TZID=AEST:20241129T094500 DESCRIPTION:Introduction\nAs social media usage continues to proliferate\, platforms have become hotspots for recruitment into modern slavery. While research has predominately focused on recruitment into forced sexual explo itation\, some efforts have been made to analyse recruitment into forced l abour by utilising computational methods to gather and analyse online comm unications related to modern slavery activities (Williams\, Burnap\, & Slo an\, 2017)\, albeit with limited attention to social media practices. Fill ing this gap is important to combating modern slavery as forced labour in the private economy makes up the largest share of global modern slavery (3 5%) and the majority of all forced labour cases (63%) (International Labou r Organization\, Walk Free & International Organization for Migration\, 20 22). Combating forced labour on social media is a significant challenge fo r stakeholders seeking to remove high-risk job advertisements\, particular ly those targeting prospective migrant workers\, as traffickers embed con tent within visual and audio materials. This covert approach allows them t o avoid detection by moderation protocols and obscures their criminal purp ose from potential victims (de Vries & Radford\, 2021).\nThis study bridge s this gap by employing a multi-modal approach that integrates visual\, au ditory\, and textual analysis. By examining social media data\, this study aims to uncover and understand the complexity of how risks of modern slav ery can present within recruitment and employment-related posts. \n\nMetho dology \nSocial media posts from six leading social media platforms (e.g.\ , Tik Tok) were collected including textual\, audio and visual data. These posts were related to job advertisements in the domestic work and constru ction sectors in the Middle East and North Africa\, with a particular focu s on countries within the Gulf Cooperation Council. Data preprocessing inc ludes removing URLs and irrelevant characters to ensure uniformity. Audio data and video on-screen text were converted into text. Non-English posts were translated into English to maintain consistency in the analysis.\nNat ural Language Processing was employed to analyse textual data and detect r isk markers of modern slavery\, such as: 1) Topic modeling was applied to uncover prevalent themes related to exploitation and slavery within the co llected data\; 2) Pattern recognition algorithms were developed to identif y specific linguistic patterns and keywords associated with potential mode rn slavery activities\, such as references to coercion\, restricted freedo m\, or abusive working conditions.\; and 3) Co-word analysis was utilised as a visual technique to detect the co-occurrence of specific terms and co ncepts\, facilitating the identification of clusters and patterns that can denote risk markers of modern slavery. \n\nConclusions\nOur research has revealed several important findings. Findings show that potential traffick ers’ social media posts format and structure tend to have more hashtags a nd limited captions\, more text and descriptions within images or videos\, and often contained multiple jobs in one advertisement post. We posit tha t these characteristics serve to appeal to as many prospective migrants as possible\, using strategies to be viewed by large audiences on social med ia platforms\, and sharing messages designed to entice and deceive. This r esearch also highlights the challenges in analysing multi-modal data to de termine whether ‘risk markers’ are present within social media posts whe re there is limited contextual data based on the sector\, country\, or lan guage. \nThis research significantly advances the fields of digital crimin ology and social media analytics by developing a novel multi-modal analyti cs approach to uncover and understand the complexity of modern slavery ris ks within recruitment social media posts. The findings can aid law enforce ment agencies\, NGOs\, and social media platforms in the detection and mit igation of modern slavery risks on social media\, contributing to global e fforts in combating this critical issue. DTSTAMP:20260308T071435Z LOCATION:Cullen Room SUMMARY:Identifying modern slavery risks on social media: a multi-modal app roach - Mingming Cheng URL:https://conferences.acspri.org.au/2024/talk/8BSAUZ/ END:VEVENT END:VCALENDAR