Quality Control: Chat Emotes

Information

This document outlines the process of quality control for chat emotes in a dataset. The key steps include:

  • Extracting and processing emote information from chat messages.
  • Generating frequency tables for emotes and the number of emotes per message.
  • Identifying and handling cases where emote names are missing.
  • Creating and saving a dictionary of unique emotes for further analysis.

Preparation

# Load packages
source(here::here("data_collection/00_02-setup-session.R"))
# Load data
chat <- qs::qread(here("local_data/chat-debates_full.qs"))

Create list of emotes

Note

For example images of the emojis visit

emotes <- chat$correct %>%
    select(message_id, message_emotes) %>%
    unnest(message_emotes) %>%
    mutate(
        emote_id = sapply(message_emotes, function(emote) emote$id),
        emote_name = sapply(message_emotes, function(emote) emote$name),
        emote_locations = sapply(message_emotes, function(emote) paste(emote$locations, collapse = ", "))
    ) %>%
    select(message_id, emote_id, emote_name, emote_locations)
emotes %>% 
    frq(emote_name, sort.frq = "desc", min.frq = 25) %>% 
    data.frame() %>% 
    select(val, frq, raw.prc, cum.prc) %>% 
    rownames_to_column("rank") %>%
    rename("Emote" = val, "n" = frq) %>%
    gt() %>% 
    gtExtras::gt_theme_538() %>% 
    tab_options(table.width = pct(80))
rank Emote n raw.prc cum.prc
1 LUL 14364 20.38 20.4
2 hasL 5640 8.00 28.4
3 bleedPurple 5171 7.34 35.7
4 Kappa 4154 5.89 41.6
5 <3 1789 2.54 44.1
6 NotLikeThis 1322 1.88 46.0
7 hasChud 1154 1.64 47.7
8 :) 1050 1.49 49.1
9 elbyBlom 1001 1.42 50.6
10 hasSlam 965 1.37 51.9
11 hasHi 840 1.19 53.1
12 WutFace 681 0.97 54.1
13 :( 675 0.96 55.1
14 hasMods 593 0.84 55.9
15 hasO 576 0.82 56.7
16 hasBoot 540 0.77 57.5
17 :D 538 0.76 58.2
18 hasRaid 456 0.65 58.9
19 hasSadge 443 0.63 59.5
20 hasBaited 384 0.54 60.1
21 hasKkona 374 0.53 60.6
22 hasHmm 368 0.52 61.1
23 PopNemo 331 0.47 61.6
24 4Head 305 0.43 62.0
25 hasCapital 292 0.41 62.4
26 has0head 286 0.41 62.8
27 hasFlex 283 0.40 63.2
28 SeemsGood 271 0.38 63.6
29 DinoDance 267 0.38 64.0
30 ResidentSleeper 261 0.37 64.4
31 TheIlluminati 255 0.36 64.7
32 R) 245 0.35 65.1
33 SUBtember 234 0.33 65.4
34 TwitchConHYPE 232 0.33 65.8
35 DoritosChip 230 0.33 66.1
36 hasWeird 226 0.32 66.4
37 Jebaited 217 0.31 66.7
38 hasPray 214 0.30 67.0
39 hasPOGGIES 213 0.30 67.3
40 hasLeft 211 0.30 67.6
41 hasRight 204 0.29 67.9
42 PogChamp 204 0.29 68.2
43 hasSmol 201 0.29 68.5
44 200 0.28 68.8
45 BatChest 196 0.28 69.0
46 :face_with_tears_of_joy: 192 0.27 69.3
47 :rolling_on_the_floor_laughing: 178 0.25 69.6
48 MrDestructoid 167 0.24 69.8
49 hasKapp 152 0.22 70.0
50 BibleThump 149 0.21 70.2
51 :coconut: 142 0.20 70.4
52 hasPause 137 0.19 70.6
53 DansGame 136 0.19 70.8
54 hasSilly 135 0.19 71.0
55 hasWut 134 0.19 71.2
56 :O 129 0.18 71.4
57 hasREE 125 0.18 71.5
58 hasWhat 124 0.18 71.7
59 dsaFP 123 0.17 71.9
60 hasChair 119 0.17 72.1
61 FailFish 117 0.17 72.2
62 vioSASS 117 0.17 72.4
63 hasNerd 112 0.16 72.6
64 hasPains 110 0.16 72.7
65 VoteYea 110 0.16 72.9
66 TransgenderPride 107 0.15 73.0
67 atpRtsd 106 0.15 73.2
68 BabyRage 106 0.15 73.3
69 L 106 0.15 73.5
70 :P 104 0.15 73.6
71 PoroSad 104 0.15 73.8
72 SMOrc 102 0.14 73.9
73 dsaL 100 0.14 74.1
74 :/ 96 0.14 74.2
75 dsaNODDERS 94 0.13 74.3
76 rathboPALESTINEHEART 94 0.13 74.5
77 hasRage 91 0.13 74.6
78 hasPOGGERS 88 0.12 74.7
79 HeyGuys 84 0.12 74.8
80 CoolStoryBob 83 0.12 75.0
81 hasRant 83 0.12 75.1
82 has5 81 0.11 75.2
83 dsaKEKW 79 0.11 75.3
84 CurseLit 77 0.11 75.4
85 BigSad 73 0.10 75.5
86 hasGun 73 0.10 75.6
87 WhySoSerious 73 0.10 75.7
88 ;) 70 0.10 75.8
89 PopCorn 69 0.10 75.9
90 hasSpooked 66 0.09 76.0
91 Squid2 65 0.09 76.1
92 atpCap 63 0.09 76.2
93 Squid1 63 0.09 76.3
94 :grinning_squinting_face: 62 0.09 76.4
95 Squid3 62 0.09 76.5
96 Squid4 62 0.09 76.5
97 hasPog 61 0.09 76.6
98 hasComfy 60 0.09 76.7
99 hasBuff 59 0.08 76.8
100 hasUnless 59 0.08 76.9
101 GoldPLZ 57 0.08 77.0
102 hasZzz 54 0.08 77.0
103 KomodoHype 54 0.08 77.1
104 GoatEmotey 53 0.08 77.2
105 hasPrime 53 0.08 77.3
106 hasStop 53 0.08 77.3
107 Shush 51 0.07 77.4
108 CoolCat 50 0.07 77.5
109 🇵🇸 49 0.07 77.5
110 dsaKnee 49 0.07 77.6
111 hasWicked 49 0.07 77.7
112 PowerUpR 49 0.07 77.8
113 CaitlynS 48 0.07 77.8
114 hasHug 48 0.07 77.9
115 HypeLUL 48 0.07 78.0
116 KappaPride 48 0.07 78.0
117 hasHAAA 47 0.07 78.1
118 ironmouseLOVE 47 0.07 78.2
119 sL 47 0.07 78.2
120 strug4Free 47 0.07 78.3
121 FBCatch 46 0.07 78.4
122 OSFrog 46 0.07 78.4
123 PowerUpL 46 0.07 78.5
124 GlitchCat 45 0.06 78.6
125 hasMad 45 0.06 78.6
126 hasSmash 45 0.06 78.7
127 hasTruth 45 0.06 78.8
128 MaxLOL 45 0.06 78.8
129 ScaredyCat 44 0.06 78.9
130 :face-fuchsia-tongue-out: 42 0.06 78.9
131 hasD 42 0.06 79.0
132 OhMyDog 42 0.06 79.1
133 DatSheffy 41 0.06 79.1
134 FBtouchdown 41 0.06 79.2
135 PJSugar 41 0.06 79.2
136 TTours 41 0.06 79.3
137 :face-green-smiling: 40 0.06 79.3
138 rathboFREE 39 0.06 79.4
139 dsaCOPIUM 38 0.05 79.5
140 hasBOOMER 38 0.05 79.5
141 :fire: 37 0.05 79.6
142 :p 37 0.05 79.6
143 BloodTrail 37 0.05 79.7
144 EleGiggle 37 0.05 79.7
145 FBBlock 37 0.05 79.8
146 Keepo 37 0.05 79.8
147 rathboEST 37 0.05 79.9
148 rathboPAL 37 0.05 79.9
149 gremloeFP 36 0.05 80.0
150 hasEZ 36 0.05 80.0
151 rathboINE 36 0.05 80.1
152 :palm_tree: 35 0.05 80.1
153 rathboFREEPALESTINE 35 0.05 80.2
154 yugopnXISALUTE 35 0.05 80.2
155 strug4F12 34 0.05 80.3
156 DarkMode 32 0.05 80.3
157 dsaFacepalm 32 0.05 80.4
158 mermai40Heart 32 0.05 80.4
159 vioSLUDGE 32 0.05 80.5
160 yugopnRAID 32 0.05 80.5
161 BopBop 31 0.04 80.5
162 dsaBrooks 31 0.04 80.6
163 hasSammie 31 0.04 80.6
164 PokPikachu 31 0.04 80.7
165 PotFriend 31 0.04 80.7
166 forsenBased 30 0.04 80.8
167 :o 29 0.04 80.8
168 hasKomrade 29 0.04 80.8
169 GayPride 28 0.04 80.9
170 ironmouseSABERDANCE 28 0.04 80.9
171 PixelBob 28 0.04 81.0
172 VoHiYo 28 0.04 81.0
173 🇺🇦 27 0.04 81.0
174 CarlSmile 27 0.04 81.1
175 ckwanFartdance 27 0.04 81.1
176 MiniK 27 0.04 81.2
177 vioFP 27 0.04 81.2
178 VoteNay 27 0.04 81.2
179 yugopnPal 27 0.04 81.3
180 :orange_circle: 26 0.04 81.3
181 forsenLaughingAtYou 26 0.04 81.3
182 FortOne 26 0.04 81.4
183 hasFatty 26 0.04 81.4
184 hasGachi 26 0.04 81.5
185 KappaHD 26 0.04 81.5
186 KEKHeim 26 0.04 81.5
187 rhyzROT 26 0.04 81.6
188 :loudly_crying_face: 25 0.04 81.6
189 dsaDance 25 0.04 81.7
190 dsaHmm 25 0.04 81.7
191 ironmouseWiggly 25 0.04 81.7
192 PogBones 25 0.04 81.8
193 n < 25 12861 18.25 100.0
194 NA 0 0.00 NA 
emotes %>% 
    group_by(message_id) %>% 
    summarise( n = n()) %>% 
    frq(n) %>% 
    data.frame() %>% 
    select(val, frq, raw.prc, cum.prc) %>%
    rename("Number of Emotes" = val, "n" = frq) %>%
    gt() %>% 
    gtExtras::gt_theme_538()
Number of Emotes n raw.prc cum.prc
1 62534 94.93 94.9
2 2465 3.74 98.7
3 612 0.93 99.6
4 178 0.27 99.9
5 55 0.08 100.0
6 13 0.02 100.0
7 9 0.01 100.0
8 4 0.01 100.0
10 1 0.00 100.0
13 1 0.00 100.0
NA 0 0.00 NA

Check: “Empty emotes”

emotes %>% 
    group_by(emote_name, emote_id) %>% 
    summarise(n = n()) %>% 
    arrange(desc(n)) %>% 
    filter(emote_name == "") %>%
    gt() %>% 
    gtExtras::gt_theme_538()
`summarise()` has grouped output by 'emote_name'. You can override using the
`.groups` argument.
emote_id n
425618 53
302140936 29
555555558 21
555555584 21
1 13
555555560 11
555555580 8
306629700 5
300238152 4
302587115 3
303446392 3
emotesv2_4f058d58458544a4971de55672468204 3
300238154 2
300756431 2
555555577 2
555555589 2
300238151 1
300238155 1
301079765 1
303433990 1
303706436 1
304192517 1
354 1
489 1
508650 1
555555557 1
555555563 1
555555585 1
6 1
626795 1
62835 1
emotesv2_1c432fe325994220960ed5720682ca63 1
emotesv2_819621bcb8f44566a1bd8ea63d06c58f 1
emotesv2_89106685bdb643c2943994b027e25556 1 
emote_name_recode <- emotes %>%
    group_by(emote_id, emote_name) %>%
    summarise(n = n(), .groups = 'drop') %>%
    arrange(desc(n)) %>%
    group_by(emote_id) %>%
    filter(n == max(n)) %>%
    slice(1) %>%
    select(emote_id, emote_name)

emotes_recoded <- emotes %>%
    left_join(emote_name_recode, by = "emote_id", suffix = c("", "_recode")) %>%
    mutate(emote_name = ifelse(!is.na(emote_name_recode), emote_name_recode, emote_name)) %>%
    select(-emote_name_recode)

Create emote dictionary

# Extract unique emotes
emotes_recoded_names <- emotes_recoded %>% 
    distinct(emote_name, .keep_all = TRUE) %>% 
    arrange(emote_name) %>% 
    filter(emote_name != "") %>%
    pull(emote_name)

# Ensure the list is named
named_emotes_recoded_names <- setNames(
  as.list(emotes_recoded_names), emotes_recoded_names)

# Create a dictionary
dict_emotes <- dictionary(named_emotes_recoded_names)

# Save the dictionary to a file
saveRDS(dict_emotes, file = here(project_dir, "local_data/dictionary_chat_emotes.RDS"))