BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//conferences.acspri.org.au//2026//talk//BJU9SB 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-BJU9SB@conferences.acspri.org.au DTSTART;TZID=AEST:20261126T134500 DTEND;TZID=AEST:20261126T140000 DESCRIPTION:In online communities\, view counts\, upvote counts\, and comme nt counts indicate how users engage with posts. Posts receiving many view counts and upvote counts may be considered supported by other users\, wher eas posts with many comment counts tend to generate active discussion by p rompting additional opinions. Although comment counts can be positively co rrelated with view or upvote counts\, some users may receive high levels o f support but limited discussion\, while others may generate discussion de spite receiving relatively little support. Thus\, high support does not ne cessarily lead to active discussion\, and vice versa. Both types of users exhibit high levels of engagement with their posts and can therefore be cl assified as influencers\, but their characteristics differ depending on wh ether they primarily receive support from other users or encourage additio nal discussion. Consequently\, treating them as a single type of influence r may obscure the distinct influences and roles that each type plays withi n the community. Accordingly\, this study classifies influencers into thre e types based on whether they meet criteria for the level of support (view counts /upvote counts) and interaction (comment-based conversation networ ks)\, and aims to identify how the characteristics of the posts they creat e differ across these types.\nUsing data from the Politics/Current Affairs board of FMKorea\, one of the largest online communities in South Korea\, this study constructs a conversation network based on post–comment and comment–reply relationships. The conversation network constructed from 2 0\,306 posts and 87\,162 comments consists of 4\,765 nodes and 52\,458 edg es. Users with high in-eigenvector centrality are classified as “interac tion influencers\,” while users receiving high levels of support based o n view counts and upvote counts are classified as “support influencers. ” Users meeting both criteria are classified as “super influencers.” This approach enables a comparison between users who occupy central posit ions in discussions via posts and comments and those who receive support f rom the other user. To compare the three influencer groups\, we conduct mu ltiple regression quadratic assignment procedure (MRQAP) analyses using tw o dummy-coded group variables as independent variables. We estimate separa te models in which the dependent variable is pairwise similarity in one of four post characteristics: post length\, word use\, the use of external l inks\, and the number of comments authors leave on their own posts. These analyses examine whether similarities in post characteristics differ by in fluencer group. \nAs an expected result of this study\, the similarity in word use is expected to decrease significantly among interaction influence rs compared to support influencers. The use of common vocabulary\, such as popular expressions used within the community\, may help influencers rece ive more support from other users. The similarity in the use of external l inks is expected to be higher among interaction influencers than among sup port influencers. Providing information from external sources in posts may encourage users to participate in further discussions through comments. I nteraction influencers and super influencers are also expected to have mor e similar post characteristics to each other than to support influencers.\ nThis study contributes to the literature by distinguishing influencers fr om two perspectives—levels of support and the formation of interactive d iscussions—in order to account for their distinct roles their distinct r oles\, and by identifying how the characteristics of posts created by infl uencers who receive support differ from those of influencers who lead disc ussions. From a social perspective\, this study provides insight into the different roles that influencers may play in the process of opinion format ion in anonymous online communities\, such as sharing information from ext ernal sources and encouraging interaction. DTSTAMP:20261013T070024Z LOCATION:MacCallum Room SUMMARY:Who Receives Support\, and Who Generates Interaction?: Examining In fluencer Types and Post Characteristics Using MRQAP Analysis - LeeSeongEun URL:https://conferences.acspri.org.au/2026/talk/BJU9SB/ END:VEVENT END:VCALENDAR