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step 3.2parison between Users which have Pets and Users instead of Dogs

step 3.2parison between Users which have Pets and Users instead of Dogs

step 3.2parison between Users which have Pets and Users instead of Dogs

Socio-demographic investigation and you can amount of reputation photo showed for everyone analysed profiles (letter = 2400) and you will alone getting Vienna (n = 1200) and Tokyo (letter = 1200).

Of the 2400 investigated profiles, 373 (15.5%) displayed at least one animal photo. In both cities, we found a positive correlation between the number of profile photos and the number of profile photos showing animals (Vienna: rs = 0.184; p = 0.008 | Tokyo: rs = 0.206; p = 0.009).

Comparison of the users who displayed animal photos on their profile and the users who did not do so resulted in the following significant differences (see Desk 5 ). On the selected analysed dating app, significantly more women than men (p = 0.049) present animal photos on their profiles. Further, significantly more users in Vienna (p = 0.006), and significantly more older users (p = 0.019), have profiles with animal photos as compared with users in Tokyo and younger users. In addition, users who display an animal photo on their profile post, on average, display one more photo than users who do not do so (p < 0.001). No significant differences between heterosexual and homosexual users of the analysed app were identified (p = 0.639) (see Table 5 ).

Table 5

Socio-group study and you can number of reputation images presented for everyone pages with dogs (n = 373) and you will profiles rather than pet (n = 2027).

3.step three. Frequency and you may Classification from Pets Displayed for the Pages

A much deeper reason for the study was to regulate how of a lot users showed pets and you can what forms of creature was basically shown. Overall, a lot more users during the Vienna (211; 17.6%) tell you animals to their reputation than users inside the Tokyo (162; thirteen.5%) (? dos (1) = seven.622; p = 0.006). The gaydar username profiles-i.elizabeth., 77.7% when you look at the Vienna and 76.5% inside the Tokyo-presented the pet, or animals, to your just one reputation photo. When you look at the an inferior proportion regarding instances-we.e., 22.3% in Vienna and you can 23.5% during the Tokyo-the new profiles got multiple photo proving your pet, otherwise dogs, within profile.

step three.step 3.1. Presentation from Animals in the 1st Profile Images

Of 373 pages deciding to become creature pictures, 73 (19.6%) showed the brand new dogs on their very first character pictures. Right here, comparison out-of profiles within the Vienna and you will Tokyo found tall variations as 65.9% profiles in the Vienna displayed a puppy towards the basic photographs given that compared to 31.3% away from users inside Tokyo (? dos (1) = 8.610, p = 0.003). And also, simply users within the Vienna (12.2%) have shown ranch animals towards first character photo. That it contributed to an improvement to help you profiles in the Tokyo (? dos (1) = cuatro.189, p = 0.041). I including found that way more pages within the Tokyo showed kitties (40.6%) and you may exotic pet (15.6%) within very first character photo than simply profiles inside the Vienna (kitties = 2.4%; unique pet = 0.0%) (cats: ? dos (1) = eight.819, p = 0.005; amazing animals: ? dos (1) = six.877, p = 0.009).

3.step 3.dos. Presentation out-of Pets in every Reputation Photos (Such as the Very first Profile Photos)

Figure 1 shows the percentages of various animal species shown on the analysed profiles. Again, comparison between the profiles in Vienna and Tokyo revealed significant differences here. Users in Tokyo were significantly more likely to show cats (35.8%) and small animals (6.8%) than users in Vienna (cats = 18.0%; small animals = 0.0%) (cats: ? 2 (1) = , p < 0.001; small animals: ? 2 (1) = , p < 0.001). The Viennese profiles included farm animal (10.9%) and horse (7.1%) photos significantly more often than the profiles in Tokyo (farm animals = 0.6%; horses = 1.2%) (farm animals: ? 2 (1) = , p < 0.001; horses: ? 2 (1) = 7.270, p = 0.007) (see Figure 1 ).

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