Exploring the Gendered Discourse of Criticism on Microcelebrities - COMU3120 - Digital Analytics - clazaria
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Exploring the
Gendered Discourse
of Criticism on COMU3120 – Digital Analytics
Microcelebrities
By: Clarissa Azaria Dharmaseta (43783823)
Clarissa Azaria Dharmaseta (43783823) COMU3120 – Digital Analytics
Table of Contents
INTRODUCTION .................................................................................................................................3
METHOD ...............................................................................................................................................4
RESULTS ...............................................................................................................................................5
DISCUSSION .......................................................................................................................................11
WORKS CITED ..................................................................................................................................12
APPENDIX ...........................................................................................................................................14
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Clarissa Azaria Dharmaseta (43783823) COMU3120 – Digital Analytics
Introduction
Traditionally, there has been a clear gender bias in mainstream media reporting of celebrities,
where wrongdoings of women are more highly surveilled, more readily problematised and
more heavily scrutinised than their male counterparts (Rojek, 2001, p. 174). Gies’ (2011) finds
that female celebrities are much more likely to be the target of punitive media commentary (p.
358). Gossip culture puts female celebrities at their forefront, hyper-scrutinising their ageing,
cosmetic procedures, bodies and sexuality (Fairclough, 2012).
While these studies focus on scrutiny of celebrities in traditional media, there is a gap in the
literature on online criticism of microcelebrities. Davina Rankin, a female contestant on the
popular dating show, Married At First Sight, speaks out about the hurtful and threatening online
comments following the airing of the show (9Now, 2018). Similarly, contestant Sarah Roza,
slammed body shamers on Instagram after an internet troll told her to “stop showing [her]
freakin tits” (Nsenduluka, 2018). More recently, Tracey Jewel (2018) points out the unfair
criticism on her decision to move away from her daughter to be with her new partner, whilst
male contestant Telv Williams does the same without public scrutiny (Jewel, 2018).
The current study, which explores the gendered discourse of online criticism of reality
television stars, is prompted by the gender-biased criticism towards female contestants on
Married At First Sight, aiming to discover the differences between how each gender is talked
about online.
Research question: How does online criticism of male and female contestants differ on the
Married At First Sight Australia (MAFS) Facebook page?
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Clarissa Azaria Dharmaseta (43783823) COMU3120 – Digital Analytics
Method
The Married At First Sight (@MarriedAU) Facebook page was used to inform this study.
Facebook was chosen as the MAFS page was highly active throughout the season. Generally,
content consisted of controversial recaps or sneak peaks that prompted heated conversation in
the comments.
Using Social Scraping Tool Reaper (2018), 15 comments from each of the 650 posts between
the seven weeks that the show aired (29th January-21st March 2018) were obtained. Any data
from before and after these dates were deleted. The number of posts throughout the season was
rounded to 650, taken from the average of 13 times a day for 49 days. Approximately 10,000
data sets were acquired.
Leximancer (2018) was used to analyse comments and visualise how certain concepts are
specifically related to each gender. Rather than individually deducing concepts from each
contestant, compound concepts were created to group the male and female contestants. This
way, related concepts between the groups can be distinguished.
To find out whether concepts related to each group were generally positive or negative, a
sentiment lens was applied. An insights dashboard was generated to identify the prominence
of favourable and unfavourable terms for each gender’s group. To support this, another insights
dashboard was created to identify prominence scores of individual contestants to investigate
how certain individuals attributed to the group score.
A concept map was created to visualise the key concepts that appeared in the data. The
compound concepts for the two genders were then individually selected to see which related
word-like concepts appeared.
Related word-like concepts from each gender group were analysed. While some concepts were
chosen due to highest likelihood percentages, others are chosen to analyse varied
conversational themes from both negative and positive concepts.
From this, the comments from each chosen related-concept was studied, identifying key themes,
categorising themes as criticism or praise, and which contestant(s) they were directed to.
Likelihood percentages are deduced from the number of times the theme appears, as a
percentage to the number of total comments within the selected concept. Comments that did
not fit into a particular theme were disregarded in the results.
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Clarissa Azaria Dharmaseta (43783823) COMU3120 – Digital Analytics
Results
Figure 1. Leximancer Visualisation Map
The concept map’s most prominent concepts are look, love, and men’s names.
Table 1. Ranked Concepts for Men's and Women's Group
Favourable terms are more commonly present than unfavourable terms in both genders. The
overall presence of favourable and unfavourable terms, however, is very similar (9/13 vs.
10/14), explaining the uniform prominence score of 1.2.
5Clarissa Azaria Dharmaseta (43783823) COMU3120 – Digital Analytics
Table 2. Ranked Concepts of Individual Female Contestants
Tracey, Charlene and Carly are contestants that have relatively equal favourable and
unfavourable associations, while the rest have highly differing scores. Gabrielle and Sarah have
the highest favourable scores in the group, with 2.1 and 1.9 respectively. This is contrasted by
their low unfavourable scores of both 0.7. Similarly, Ashley and Davina top the unfavourable
scores with 1.8 and 1.5. While Ashley’s score is slightly higher, the difference between
Davina’s two scores are larger at 0.9 compared to the Ashley’s difference of 0.5.
6Clarissa Azaria Dharmaseta (43783823) COMU3120 – Digital Analytics
Table 3. Ranked Concepts of Individual Male Contestants
Dean is the only male to have a significantly higher unfavourable score compared to his
favourable score. While Troy was found to have the same unfavourable score as Dean, this was
met with the same favourable score, putting him under the same category of similar scoring
concepts as Ryan and Justin. Telv, Patrick and Nasser had much higher favourable scores than
unfavourable scores.
7Clarissa Azaria Dharmaseta (43783823) COMU3120 – Digital Analytics
Query: WORD:women's_names AND WORD:mouth (COUNT: 22)
Criticism/Praise Key Themes Contestant Likelihood
Criticism ‘Big mouth’ Charlene 41%
Criticism Plastic surgery Davina 32%
Criticism Lying General 9%
Praise Outspoken nature Charlene 18%
Query: WORD:women's_names AND WORD:disgusting (COUNT: 15)
Criticism/Praise Key Themes Contestant Likelihood
Criticism Manipulative actions Davina 47%
Criticism Slut-shaming Davina 20%
Criticism Body-shaming Sarah 7%
“ “ Davina 7%
Query: WORD:women's_names AND WORD:upset (COUNT: 14)
Criticism/Praise Key Themes Contestant Likelihood
Criticism Reaction to wife-swap Ashley 36%
Criticism Sensitive nature Sarah 21%
Criticism Manipulative actions Davina 14%
“ “ Carly 14%
Query: WORD:women's_names AND WORD:strong(COUNT: 12)
Criticism/Praise Key Themes Contestant Likelihood
Criticism Lack of strength Tracey 25%
Strength is
Criticism ‘intimidating’ or Charlene 17%
‘overbearing’
Praise Strength as a woman Charlene 33%
“ “ Gabrielle 25%
Query: WORD:women's_names AND WORD:lips (COUNT: 10)
Criticism/Praise Key Themes Contestant Likelihood
Criticism Plastic surgery Davina 70%
Criticism Habit of licking lips Tracey 30%
Table 4. Themes from Related-Concepts to Female Contestant Names
Davina is consistently criticised for her plastic surgery and manipulative actions. Tracey is also
criticised for her lip fillers. Ashley is harshly criticised for her reaction to ex-husband’s wife-
swap with Carly, while she was marginally criticised for not warning her before the reunion
show. Charlene is more often criticised for her ‘big-mouth’, but also praised for speaking up
against certain actions.
8Clarissa Azaria Dharmaseta (43783823) COMU3120 – Digital Analytics
Sarah is criticised for her plastic surgery and sensitive nature, however, the tone of criticism
towards her is much less aggressive compared to Davina, Tracey, Ashley or Charlene. An
example of this is, “Sarah seems like a lovely person but I think she's far too sensitive…”
(Martin, 2018). Gabrielle has the highest favourable prominence score and is heavily praised
for her strength in staying with her difficult partner, while Tracey is criticised for a lack of
strength while doing the same.
Query: WORD:men's_names AND WORD:lies (COUNT: 16)
Criticism/Praise Key Themes Contestant Likelihood
Criticism Habitual lying Dean 44%
“ “ Nasser 6%
Criticism Unawareness of filming Dean 38%
Query: WORD:men's_names AND WORD:blame (COUNT: 17)
Criticism/Praise Key Themes Contestant Likelihood
Criticism Unapologetic for actions Dean 18%
Criticism Overbearingness Troy 12%
Praise Handling of situations Ryan 12%
Undeserving of
Praise Dean 12%
all the blame
Query: WORD:men's_names AND WORD:upset (COUNT: 19)
Criticism/Praise Key Themes Contestant Likelihood
Criticism Lack of common sense Troy 5%
Praise Moving on Troy 37%
Praise Patience with partner Telv 26%
Praise Comedic value Ryan 10%
Query: WORD:men's_names AND WORD:change (COUNT: 41)
Criticism/Praise Key Themes Contestant Likelihood
Criticism Inability to change Dean 23%
Sympathy for being
Praise Ryan 39%
unable to change vote
Sympathy for being with
Praise Troy 15%
ex-partner
“ “ Justin 5%
Praise Sacrifices for partner Telv 10%
Praise Likable contestant Ryan 7%
Table 5. Themes from Related-Concepts to Male Contestant Names
9Clarissa Azaria Dharmaseta (43783823) COMU3120 – Digital Analytics
Themes from the male-related concepts are much less focused on criticism. While Dean is a
large target of criticism for his actions, comments lacked personal attacks, as the women
experienced. The only instance of this is the theme of overbearingness from Troy, however,
similar to Sarah, the tone of criticism is much softer, e.g. “He seemed nice but also really
annoying” (David, 2018). In these concepts, Ryan is given a lot of praise and empathy in the
comments, which is not highly reflective of his fairly similar favourability scores. Telv’s high
praise is reflective of his high scoring favourable score. Justin and Patrick did not appear in
comments under these concepts, which is supported by their strength scores of 1% and below,
implying they are not central individuals in the discussion.
10Clarissa Azaria Dharmaseta (43783823) COMU3120 – Digital Analytics
Discussion
Collectively, both male and female groups were not found to have a significant difference in
favourability prominence scores (See Table 1), implying criticism is not wholly targeted
towards one group. While there are themes of criticism specific to each group, it is found that
criticism is more so dependent on the caricatures created by the reality television producers.
Younger (See Table 6 in Appendix), outspoken women with recognisable plastic surgery are
the main targets of criticism in female stars. An example of this is Davina, who is framed to be
an obvious ‘villain’ on the show. Similarly, Ashley has the highest unfavourable scores for her
reaction to her ex-partner’s wife swap. When criticised for their actions, the tone is usually
aggressive and personal towards their physical appearances.
Contrastingly, emotionally-vulnerable, older, ‘natural’-looking women are pushed to be ‘fan
favourites’. Tracey is emotionally-vulnerable, but because she is younger and is criticised for
her plastic surgery, fan reactions are mixed. Sarah is often criticised for her sensitivity, and
although she is also criticised for plastic surgery, she still has one of the highest favourable
scores. This suggests that age is perhaps the strongest defining factor of criticism in women,
followed by visible plastic surgery. This is supported by Gabrielle’s high favourability score
and praise. She is soft-spoken, older, and is one of the few female contestants that was not
criticised for plastic surgery.
Male contestants were mainly criticised for their actions, without a clear pattern of other
determining factors such as age or appearance. Dean is singled out as the ‘male villain’ from
the criticism themes of manipulation and a high unfavourable prominence score. Comments
detract very rarely from his actions. There does not seem to be a clear pattern between
contestants with significantly higher favourable scores either. While ‘fan favourites’ Telv and
Patrick are of similar ages, Nasser is significantly out of their age range.
In conclusion, certain caricatures of women are targeted more harshly and personally, while
men are generally criticised solely based on their actions as people are less reliant on male
caricatures when forming criticism.
A strength of this study is the manual categorisation of themes from comments of concepts
rather than simply analysing the related-concepts of each gender group as it helps understand
the context of the relationship. This is particularly relevant as male and female names are often
mentioned in conjunction in the comments, so a human eye needs to distinguish which gender
or contestant the concepts refer to. A limitation of this is researcher bias in manual
categorisation. Another is the automatic categorisation of favourable and unfavourable
concepts, analysed in the insights dashboard.
11Clarissa Azaria Dharmaseta (43783823) COMU3120 – Digital Analytics
Works Cited
David, J. E. (2018). Troy and Carly seem really happy together but I don't blame Ashley
for being snappy at Troy during the experiment. He seemed nice but also really annoying.
Ashley probably didn't have the patience to tolerate it [Facebook post comment]. Retrieved
from
https://www.facebook.com/MarriedAU/posts/1748326741890693?comment_id=1748361985
220502
Fairclough, K. (2012). Nothing less than perfect: female celebrity, ageing and hyper-
scrutiny in the gossip industry. Celebrity Studies, 3(1), 90-103, DOI:
10.1080/19392397.2012.644723
Jewel, T. (2018, April 8). ‘Everyone thinks I’m abandoning my child’. News.com.au.
Retrieved from http://www.news.com.au/
Lieve, G. (2011). Stars Behaving Badly. Feminist Media Studies, 11(3), 347-361, DOI:
10.1080/14680777.2010.535319
Martin, L. (2018). Sarah seems like a lovely person but I think she's far too sensitive,
the relationship won't work if she's going to get upset every time her partner says or does
something she doesn't like. She got stuck into Telv only a day or 2 after they'd met when she
checked his phone and found he hadn't removed his dating apps. She needs to lighten up
because Telv seems like a fairly easy going guy until he gets pushed too far [Facebook post
comment]. Retrieved from
https://www.facebook.com/MarriedAU/videos/1729044550485579/?comment_id=17290768
33815684
9Now. (2018). Davina: Diva or misunderstood? A Current Affair. [Video file].
Retrieved from https://www.9now.com.au/a-current-affair/2018/extras/latest/180326/davina-
diva-or-misunderstood/?ocid=Social-ACA
Nsenduluka, B. (2018, March 21). 'I'll wear what I want!' MAFS' Sarah Roza SLAMS
bodyshamers after a troll told the star to 'stop showing your freaking tits'... as she admits having
size '10G boobs' is not easy. Daily Mail Australia. Retrieved from
http://www.dailymail.co.uk/auhome/index.html
Rojek, C. (2001). Celebrity, Reaktion Books, London.
Smith, A. (2018). Leximancer (V4.50.27) [Computer software]. Retrieved from
https://info.leximancer.com/
12Clarissa Azaria Dharmaseta (43783823) COMU3120 – Digital Analytics
Smith, A. (2018). Reaper (V.2.5.4) [Computer software]. Retrieved from
https://github.com/ScriptSmith/reaper
13Clarissa Azaria Dharmaseta (43783823) COMU3120 – Digital Analytics
Appendix
Contestant Age
Ryan 29
Telv 33
Troy 34
Patrick 34
Dean 39
Justin 41
Nasser 50
Davina 26
Ashley 28
Carly 32
Charlene 33
Tracey 34
Sarah 38
Gabrielle 44
Table 6. Ages of Contestants
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