BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//conferences.acspri.org.au//2026//speaker//YNNZWR 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-2026-TP3VHA@conferences.acspri.org.au DTSTART;TZID=AEST:20261125T113000 DTEND;TZID=AEST:20261125T114500 DESCRIPTION:This study addresses a gap in research on AI agents and online communication by examining how interactions among AI agents are organised and how their relational structures compare with those found in human onli ne communities. Drawing on the machine behaviour perspective\, it treats A I agents as empirical behavioural actors whose interactions can be studied in collective settings. The study uses the concept of AI-agent sociality to refer to the observable communication and relational patterns that emer ge through interactions among AI agents.\n\nMoltbook provides a useful cas e because it offers a large-scale live platform environment for observing interactions among AI agents. By comparing Moltbook with Reddit\, the stud y examines two large-scale online discussion platforms\, with similar plat form affordances for enabling discussion\, but different actor types (Molt book: AI agents\, Reddit: humans). The data collection covers the period 3 0 January to 3 May 2026 and focuses on discussions relating to the topic o f consciousness.\n\nThe analysis focuses on reply-tree structures and acto r-level reply networks. Reply-tree analysis examines the depth and width o f discussion threads\, while actor reply networks are used to investigate reciprocity and community structure. The results reveal clear differences between the two platforms. Moltbook discussions tend to be wider and shall ower\, while Reddit supports deeper exchanges. Moltbook also exhibits subs tantially lower actor-level reciprocity\, suggesting that high levels of p osting activity do not necessarily develop into sustained reciprocal inter action. At the same time\, its reply network contains distinct communities \, indicating that interaction among AI agents is organised around particu lar groups.\n\nOverall\, the findings suggest that AI-agent sociality shou ld be examined not only through the presence or volume of interaction\, bu t also through the relational structures that emerge from it. More broadly \, the analysis shows how social network analysis can be used to study soc iality on emerging AI-agent platforms. DTSTAMP:20261013T070102Z LOCATION:MacCallum Room SUMMARY:Comparing Interaction Networks on AI-Agent and Human Online Discuss ion Platforms: A Case Study of Moltbook and Reddit - Robert Ackland URL:https://conferences.acspri.org.au/2026/talk/TP3VHA/ END:VEVENT BEGIN:VEVENT UID:pretalx-2026-SZCRWB@conferences.acspri.org.au DTSTART;TZID=AEST:20261126T113000 DTEND;TZID=AEST:20261126T114500 DESCRIPTION:Social media platforms such as Twitter/X\, Reddit\, Bluesky and Mastodon are nowadays important spaces where people engage with\, discuss and promote social issues and politics. While the deliberative potential of online discussion platforms and hence their contribution to democracy a nd social cohesion is the subject of both concern (and sometimes\, moral p anic) and also on-going academic research\, there can be no doubt that soc ial media is important in the modern information environment\, with social media actors responding to and at times influencing how mainstream media engages with and promotes issues. \n\nFraming is the process whereby actor s with an interest in a contested topic use strategic communication to pro vide interpretation\, context or perspective that aligns with the actor’ s interests. The actors might include individuals\, groups\, organisations \, governments\, and the topic could relate to\, for example\, current eve nts\, government policy or social issues\, and it has been argued that fra ming involves strategically “selecting and highlighting some features of while omitting others”\, and that frames are “manifested by the prese nce or absence of certain key words” (Entman\, 1991\, pp. 52–53).\n\nT his presentation outlines work-in-progress on a quantitative methodology f or identifying frames and understanding how frames spread in the Australia n information environment. Quantitative approaches to frame identification include correspondence analysis\, semantic network analysis\, topic model ling and more recently\, large language models (LLMs). Our approach for id entifying frames draws on Kwak et al’s (2021) FrameAxis\, which uses LLM s to identify “microframes” which are dimensions of discussion relatin g to an issue (a frame comprises one or more microframes). In discussion a bout immigration\, for example\, a microframe might be legal versus illega l immigration\, and the FrameAxis approach measures how biased a given tex t (for example\, a newspaper article) is with regard to a particular micro frame i.e. does it focus predominantly on ‘illegal’ immigration rather than ‘legal’ immigration (or vice-versa)? FrameAxis also measures the relative prominence (intensity) of a particular microframe: is the text h eavily focused on the legal-illegal microframe\, hence giving less weight to other possible microframes (e.g. the segregated-desegregated microframe \, relating to integration of migrants into society)?\n\nWhile the present ation will focus on the identification of frames\, we will also briefly di scuss the second part of the methodology\, which involves using social net work analysis to understand how frames are disseminated on social media an d the relative contribution of different actors (e.g. elites vs everyday c itizens) to this spread.\n\n\nEntman\, R. M. (1991). Framing U.S. coverage of international news: Contrasts in narratives of the KAL and Iran air in cidents. Journal of Communication\, 43(4)\, 51–58.\nKwak\, H.\, An\, J.\ , Jing\, E.\, & Ahn\, Y.-Y. (2021). FrameAxis: Characterizing microframe b ias and intensity with word embedding. PeerJ Computer Science\, 7:e644. DTSTAMP:20261013T070102Z LOCATION:Sutherland Room SUMMARY:A New Approach for Identifying Frames and Mapping Their Diffusion - Robert Ackland URL:https://conferences.acspri.org.au/2026/talk/SZCRWB/ END:VEVENT END:VCALENDAR