Bermain Twitter dengan R (Part 2) – Finding Trending Topics

Data Scientist, Data Science, Machine Learning, Statistics, Data Science Indonesia, Data Analytics, Data Analysis, Data Analyst, Data, Astronomy, Astronomer, Science, Python, iPython, Jupyter Notebook, R, RStudio, Excel, Coding, Koding, Cara Mengolah Data, Mengolah Data, Olah Data, Programming, Pemrograman, Sains, Teknologi, Ilmu Data, Teknologi Informasi, Tech in Asia, Teknologi, Technology, Sains, Bisnis, Business, Business Analyst, Business Analysis, Social Media Mining, Movie Review, Muhammad Azizul Hakim, Aziz

“Social media makes it extraordinarily easy to join crusades, express solidarity and outrage, and shun traitors. Facebook was founded in 2004, and since 2006 it has allowed children as young as 13 to join. This means that the first wave of students who spent all their teen years using Facebook reached college in 2011, and graduated from college only this year.

These first true “social-media natives” may be different from members of previous generations in how they go about sharing their moral judgments and supporting one another in moral campaigns and conflicts. We find much to like about these trends; young people today are engaged with one another, with news stories, and with prosocial endeavors to a greater degree than when the dominant technology was television. But social media has also fundamentally shifted the balance of power in relationships between students and faculty; the latter increasingly fear what students might do to their reputations and careers by stirring up online mobs against them.”

~ Greg Lukianoff, Jonathan Haidt, “The Coddling of the American Mind”, The Atlantic, (September 2016).

Halo, apa kabar semuanya? :*

Sebagai lanjutan dari “Bermain Twitter dengan R, Part 1”, di postingan kali ini, kita akan membahas tips & trick yang insyaAlloh akan lebih berfaedah lagi, yaitu cara memperoleh trending topic di suatu daerah tertentu, maupun di seluruh dunia!

Sebelumnya, kita perlu mengakses twitter API terlebih dahulu, menggunakan API credentials dengan cara seperti dijabarkan pada Part 1. Jika sudah berhasil mengakses kembali aplikasi twitter kita, maka jalankanlah script berikut ini, untuk mengetahui available trend location, atau lokasi yang tersedia layanan untuk kita pantau trending topicsnya. 🙂

#return data frame with name, country and woeid
Locs <- availableTrendLocations()

#print data frame with name, country and woeid
Locs

Berikut penampakannya di R Console:

Data Scientist, Data Science, Machine Learning, Statistics, Data Science Indonesia, Data Analytics, Data Analysis, Data Analyst, Data, Astronomy, Astronomer, Science, Python, iPython, Jupyter Notebook, R, RStudio, Excel, Coding, Koding, Cara Mengolah Data, Mengolah Data, Olah Data, Programming, Pemrograman, Sains, Teknologi, Ilmu Data, Teknologi Informasi, Tech in Asia, Teknologi, Technology, Sains, Bisnis, Business, Business Analyst, Business Analysis, Social Media Mining, Movie Review, Muhammad Azizul Hakim, Aziz

Sumber Gambar: Pengalaman Pribadi.

Dengan list lengkap lokasinya, saya cantumkan di bawah ini, untuk mempermudah sodara-sodari sekalian untuk melihatnya:

name country woeid
1 Worldwide 1
2 Winnipeg Canada 2972
3 Ottawa Canada 3369
4 Quebec Canada 3444
5 Montreal Canada 3534
6 Toronto Canada 4118
7 Edmonton Canada 8676
8 Calgary Canada 8775
9 Vancouver Canada 9807
10 Birmingham United Kingdom 12723
11 Blackpool United Kingdom 12903
12 Bournemouth United Kingdom 13383
13 Brighton United Kingdom 13911
14 Bristol United Kingdom 13963
15 Cardiff United Kingdom 15127
16 Coventry United Kingdom 17044
17 Derby United Kingdom 18114
18 Edinburgh United Kingdom 19344
19 Glasgow United Kingdom 21125
20 Hull United Kingdom 25211
21 Leeds United Kingdom 26042
22 Leicester United Kingdom 26062
23 Liverpool United Kingdom 26734
24 Manchester United Kingdom 28218
25 Middlesbrough United Kingdom 28869
26 Newcastle United Kingdom 30079
27 Nottingham United Kingdom 30720
28 Plymouth United Kingdom 32185
29 Portsmouth United Kingdom 32452
30 Preston United Kingdom 32566
31 Sheffield United Kingdom 34503
32 Stoke-on-Trent United Kingdom 36240
33 Swansea United Kingdom 36758
34 London United Kingdom 44418
35 Belfast United Kingdom 44544
36 Santo Domingo Dominican Republic 76456
37 Guatemala City Guatemala 83123
38 Acapulco Mexico 110978
39 Aguascalientes Mexico 111579
40 Chihuahua Mexico 115958
41 Mexico City Mexico 116545
42 Ciudad Juarez Mexico 116556
43 Nezahualcóyotl Mexico 116564
44 Culiacán Mexico 117994
45 Ecatepec de Morelos Mexico 118466
46 Guadalajara Mexico 124162
47 Hermosillo Mexico 124785
48 León Mexico 131068
49 Mérida Mexico 133327
50 Mexicali Mexico 133475
51 Monterrey Mexico 134047
52 Morelia Mexico 134091
53 Naucalpan de Juárez Mexico 134395
54 Puebla Mexico 137612
55 Querétaro Mexico 138045
56 Saltillo Mexico 141272
57 San Luis Potosí Mexico 144265
58 Tijuana Mexico 149361
59 Toluca Mexico 149769
60 Zapopan Mexico 151582
61 Mendoza Argentina 332471
62 Santiago Chile 349859
63 Concepcion Chile 349860
64 Valparaiso Chile 349861
65 Bogotá Colombia 368148
66 Cali Colombia 368149
67 Medellín Colombia 368150
68 Barranquilla Colombia 368151
69 Quito Ecuador 375732
70 Guayaquil Ecuador 375733
71 Caracas Venezuela 395269
72 Maracaibo Venezuela 395270
73 Maracay Venezuela 395271
74 Valencia Venezuela 395272
75 Barcelona Venezuela 395273
76 Ciudad Guayana Venezuela 395275
77 Turmero Venezuela 395277
78 Lima Peru 418440
79 Brasília Brazil 455819
80 Belém Brazil 455820
81 Belo Horizonte Brazil 455821
82 Curitiba Brazil 455822
83 Porto Alegre Brazil 455823
84 Recife Brazil 455824
85 Rio de Janeiro Brazil 455825
86 Salvador Brazil 455826
87 São Paulo Brazil 455827
88 Campinas Brazil 455828
89 Fortaleza Brazil 455830
90 Goiânia Brazil 455831
91 Manaus Brazil 455833
92 São Luís Brazil 455834
93 Guarulhos Brazil 455867
94 Córdoba Argentina 466861
95 Rosario Argentina 466862
96 Barquisimeto Venezuela 468382
97 Maturín Venezuela 468384
98 Buenos Aires Argentina 468739
99 Gdansk Poland 493417
100 Kraków Poland 502075
101 Lodz Poland 505120
102 Poznan Poland 514048
103 Warsaw Poland 523920
104 Wroclaw Poland 526363
105 Vienna Austria 551801
106 Cork Ireland 560472
107 Dublin Ireland 560743
108 Galway Ireland 560912
109 Bordeaux France 580778
110 Lille France 608105
111 Lyon France 609125
112 Marseille France 610264
113 Montpellier France 612977
114 Nantes France 613858
115 Paris France 615702
116 Rennes France 619163
117 Strasbourg France 627791
118 Toulouse France 628886
119 Berlin Germany 638242
120 Bremen Germany 641142
121 Dortmund Germany 645458
122 Dresden Germany 645686
123 Dusseldorf Germany 646099
124 Essen Germany 648820
125 Frankfurt Germany 650272
126 Hamburg Germany 656958
127 Cologne Germany 667931
128 Leipzig Germany 671072
129 Munich Germany 676757
130 Stuttgart Germany 698064
131 Bologna Italy 711080
132 Genoa Italy 716085
133 Milan Italy 718345
134 Naples Italy 719258
135 Palermo Italy 719846
136 Rome Italy 721943
137 Turin Italy 725003
138 Den Haag Netherlands 726874
139 Amsterdam Netherlands 727232
140 Rotterdam Netherlands 733075
141 Utrecht Netherlands 734047
142 Barcelona Spain 753692
143 Bilbao Spain 754542
144 Las Palmas Spain 764814
145 Madrid Spain 766273
146 Malaga Spain 766356
147 Murcia Spain 768026
148 Palma Spain 769293
149 Seville Spain 774508
150 Valencia Spain 776688
151 Zaragoza Spain 779063
152 Geneva Switzerland 782538
153 Lausanne Switzerland 783058
154 Zurich Switzerland 784794
155 Brest Belarus 824382
156 Grodno Belarus 825848
157 Gomel Belarus 825978
158 Minsk Belarus 834463
159 Riga Latvia 854823
160 Bergen Norway 857105
161 Oslo Norway 862592
162 Gothenburg Sweden 890869
163 Stockholm Sweden 906057
164 Dnipropetrovsk Ukraine 918981
165 Donetsk Ukraine 919163
166 Kharkiv Ukraine 922137
167 Kyiv Ukraine 924938
168 Lviv Ukraine 924943
169 Odesa Ukraine 929398
170 Zaporozhye Ukraine 939628
171 Athens Greece 946738
172 Thessaloniki Greece 963291
173 Bekasi Indonesia 1030077
174 Depok Indonesia 1032539
175 Pekanbaru Indonesia 1040779
176 Surabaya Indonesia 1044316
177 Makassar Indonesia 1046138
178 Bandung Indonesia 1047180
179 Jakarta Indonesia 1047378
180 Medan Indonesia 1047908
181 Palembang Indonesia 1048059
182 Semarang Indonesia 1048324
183 Tangerang Indonesia 1048536
184 Singapore Singapore 1062617
185 Perth Australia 1098081
186 Adelaide Australia 1099805
187 Brisbane Australia 1100661
188 Canberra Australia 1100968
189 Darwin Australia 1101597
190 Melbourne Australia 1103816
191 Sydney Australia 1105779
192 Kitakyushu Japan 1110809
193 Saitama Japan 1116753
194 Chiba Japan 1117034
195 Fukuoka Japan 1117099
196 Hamamatsu Japan 1117155
197 Hiroshima Japan 1117227
198 Kawasaki Japan 1117502
199 Kobe Japan 1117545
200 Kumamoto Japan 1117605
201 Nagoya Japan 1117817
202 Niigata Japan 1117881
203 Sagamihara Japan 1118072
204 Sapporo Japan 1118108
205 Sendai Japan 1118129
206 Takamatsu Japan 1118285
207 Tokyo Japan 1118370
208 Yokohama Japan 1118550
209 Goyang Korea 1130853
210 Yongin Korea 1132094
211 Ansan Korea 1132444
212 Bucheon Korea 1132445
213 Busan Korea 1132447
214 Changwon Korea 1132449
215 Daegu Korea 1132466
216 Gwangju Korea 1132481
217 Incheon Korea 1132496
218 Seongnam Korea 1132559
219 Suwon Korea 1132567
220 Ulsan Korea 1132578
221 Seoul Korea 1132599
222 Kajang Malaysia 1141268
223 Ipoh Malaysia 1154679
224 Johor Bahru Malaysia 1154698
225 Klang Malaysia 1154726
226 Kuala Lumpur Malaysia 1154781
227 Calocan Philippines 1167715
228 Makati Philippines 1180689
229 Pasig Philippines 1187115
230 Taguig Philippines 1195098
231 Antipolo Philippines 1198785
232 Cagayan de Oro Philippines 1199002
233 Cebu City Philippines 1199079
234 Davao City Philippines 1199136
235 Manila Philippines 1199477
236 Quezon City Philippines 1199682
237 Zamboanga City Philippines 1199980
238 Bangkok Thailand 1225448
239 Hanoi Vietnam 1236594
240 Hai Phong Vietnam 1236690
241 Can Tho Vietnam 1252351
242 Da Nang Vietnam 1252376
243 Ho Chi Minh City Vietnam 1252431
244 Algiers Algeria 1253079
245 Accra Ghana 1326075
246 Kumasi Ghana 1330595
247 Benin City Nigeria 1387660
248 Ibadan Nigeria 1393672
249 Kaduna Nigeria 1396439
250 Kano Nigeria 1396803
251 Lagos Nigeria 1398823
252 Port Harcourt Nigeria 1404447
253 Giza Egypt 1521643
254 Cairo Egypt 1521894
255 Alexandria Egypt 1522006
256 Mombasa Kenya 1528335
257 Nairobi Kenya 1528488
258 Durban South Africa 1580913
259 Johannesburg South Africa 1582504
260 Port Elizabeth South Africa 1586614
261 Pretoria South Africa 1586638
262 Soweto South Africa 1587677
263 Cape Town South Africa 1591691
264 Medina Saudi Arabia 1937801
265 Dammam Saudi Arabia 1939574
266 Riyadh Saudi Arabia 1939753
267 Jeddah Saudi Arabia 1939873
268 Mecca Saudi Arabia 1939897
269 Sharjah United Arab Emirates 1940119
270 Abu Dhabi United Arab Emirates 1940330
271 Dubai United Arab Emirates 1940345
272 Haifa Israel 1967449
273 Tel Aviv Israel 1968212
274 Jerusalem Israel 1968222
275 Amman Jordan 1968902
276 Chelyabinsk Russia 1997422
277 Khabarovsk Russia 2018708
278 Krasnodar Russia 2028717
279 Krasnoyarsk Russia 2029043
280 Samara Russia 2077746
281 Voronezh Russia 2108210
282 Yekaterinburg Russia 2112237
283 Irkutsk Russia 2121040
284 Kazan Russia 2121267
285 Moscow Russia 2122265
286 Nizhny Novgorod Russia 2122471
287 Novosibirsk Russia 2122541
288 Omsk Russia 2122641
289 Perm Russia 2122814
290 Rostov-on-Don Russia 2123177
291 Saint Petersburg Russia 2123260
292 Ufa Russia 2124045
293 Vladivostok Russia 2124288
294 Volgograd Russia 2124298
295 Karachi Pakistan 2211096
296 Lahore Pakistan 2211177
297 Multan Pakistan 2211269
298 Rawalpindi Pakistan 2211387
299 Faisalabad Pakistan 2211574
300 Muscat Oman 2268284
301 Nagpur India 2282863
302 Lucknow India 2295377
303 Kanpur India 2295378
304 Patna India 2295381
305 Ranchi India 2295383
306 Kolkata India 2295386
307 Srinagar India 2295387
308 Amritsar India 2295388
309 Jaipur India 2295401
310 Ahmedabad India 2295402
311 Rajkot India 2295404
312 Surat India 2295405
313 Bhopal India 2295407
314 Indore India 2295408
315 Thane India 2295410
316 Mumbai India 2295411
317 Pune India 2295412
318 Hyderabad India 2295414
319 Bangalore India 2295420
320 Chennai India 2295424
321 Mersin Turkey 2323778
322 Adana Turkey 2343678
323 Ankara Turkey 2343732
324 Antalya Turkey 2343733
325 Bursa Turkey 2343843
326 Diyarbakir Turkey 2343932
327 Eskisehir Turkey 2343980
328 Gaziantep Turkey 2343999
329 Istanbul Turkey 2344116
330 Izmir Turkey 2344117
331 Kayseri Turkey 2344174
332 Konya Turkey 2344210
333 Okinawa Japan 2345896
334 Daejeon Korea 2345975
335 Auckland New Zealand 2348079
336 Albuquerque United States 2352824
337 Atlanta United States 2357024
338 Austin United States 2357536
339 Baltimore United States 2358820
340 Baton Rouge United States 2359991
341 Birmingham United States 2364559
342 Boston United States 2367105
343 Charlotte United States 2378426
344 Chicago United States 2379574
345 Cincinnati United States 2380358
346 Cleveland United States 2381475
347 Colorado Springs United States 2383489
348 Columbus United States 2383660
349 Dallas-Ft. Worth United States 2388929
350 Denver United States 2391279
351 Detroit United States 2391585
352 El Paso United States 2397816
353 Fresno United States 2407517
354 Greensboro United States 2414469
355 Harrisburg United States 2418046
356 Honolulu United States 2423945
357 Houston United States 2424766
358 Indianapolis United States 2427032
359 Jackson United States 2428184
360 Jacksonville United States 2428344
361 Kansas City United States 2430683
362 Las Vegas United States 2436704
363 Long Beach United States 2441472
364 Los Angeles United States 2442047
365 Louisville United States 2442327
366 Memphis United States 2449323
367 Mesa United States 2449808
368 Miami United States 2450022
369 Milwaukee United States 2451822
370 Minneapolis United States 2452078
371 Nashville United States 2457170
372 New Haven United States 2458410
373 New Orleans United States 2458833
374 New York United States 2459115
375 Norfolk United States 2460389
376 Oklahoma City United States 2464592
377 Omaha United States 2465512
378 Orlando United States 2466256
379 Philadelphia United States 2471217
380 Phoenix United States 2471390
381 Pittsburgh United States 2473224
382 Portland United States 2475687
383 Providence United States 2477058
384 Raleigh United States 2478307
385 Richmond United States 2480894
386 Sacramento United States 2486340
387 St. Louis United States 2486982
388 Salt Lake City United States 2487610
389 San Antonio United States 2487796
390 San Diego United States 2487889
391 San Francisco United States 2487956
392 San Jose United States 2488042
393 Seattle United States 2490383
394 Tallahassee United States 2503713
395 Tampa United States 2503863
396 Tucson United States 2508428
397 Virginia Beach United States 2512636
398 Washington United States 2514815
399 Osaka Japan 15015370
400 Kyoto Japan 15015372
401 Delhi India 20070458
402 United Arab Emirates United Arab Emirates 23424738
403 Algeria Algeria 23424740
404 Argentina Argentina 23424747
405 Australia Australia 23424748
406 Austria Austria 23424750
407 Bahrain Bahrain 23424753
408 Belgium Belgium 23424757
409 Belarus Belarus 23424765
410 Brazil Brazil 23424768
411 Canada Canada 23424775
412 Chile Chile 23424782
413 Colombia Colombia 23424787
414 Denmark Denmark 23424796
415 Dominican Republic Dominican Republic 23424800
416 Ecuador Ecuador 23424801
417 Egypt Egypt 23424802
418 Ireland Ireland 23424803
419 France France 23424819
420 Ghana Ghana 23424824
421 Germany Germany 23424829
422 Greece Greece 23424833
423 Guatemala Guatemala 23424834
424 Indonesia Indonesia 23424846
425 India India 23424848
426 Israel Israel 23424852
427 Italy Italy 23424853
428 Japan Japan 23424856
429 Jordan Jordan 23424860
430 Kenya Kenya 23424863
431 Korea Korea 23424868
432 Kuwait Kuwait 23424870
433 Lebanon Lebanon 23424873
434 Latvia Latvia 23424874
435 Oman Oman 23424898
436 Mexico Mexico 23424900
437 Malaysia Malaysia 23424901
438 Nigeria Nigeria 23424908
439 Netherlands Netherlands 23424909
440 Norway Norway 23424910
441 New Zealand New Zealand 23424916
442 Peru Peru 23424919
443 Pakistan Pakistan 23424922
444 Poland Poland 23424923
445 Panama Panama 23424924
446 Portugal Portugal 23424925
447 Qatar Qatar 23424930
448 Philippines Philippines 23424934
449 Puerto Rico Puerto Rico 23424935
450 Russia Russia 23424936
451 Saudi Arabia Saudi Arabia 23424938
452 South Africa South Africa 23424942
453 Singapore Singapore 23424948
454 Spain Spain 23424950
455 Sweden Sweden 23424954
456 Switzerland Switzerland 23424957
457 Thailand Thailand 23424960
458 Turkey Turkey 23424969
459 United Kingdom United Kingdom 23424975
460 Ukraine Ukraine 23424976
461 United States United States 23424977
462 Venezuela Venezuela 23424982
463 Vietnam Vietnam 23424984
464 Petaling Malaysia 56013632
465 Hulu Langat Malaysia 56013645
466 Ahsa Saudi Arabia 56120136
467 Okayama Japan 90036018

Apa pula WOEID itu?

WOEID adalah Yahoo’s Where on Earth IDs, atau representasi lokasi yang diadopsi oleh twitter, sehingga memungkinkan kita untuk mendapatkan trending topic per lokasi yang tersedia WOEIDnya. WOEID adalah identifier 32-bit yang unik dan nonrepetitif, yang pertama kali dibuat oleh GeoPlanet yang kemudian dikelola oleh Yahoo!

Yang saya cantumkan di atas tadi adalah list seluruh WOEID yang tersedia. Sebagai contoh, untuk kota-kota di Indonesia hanya ada 11 kota, yaitu Bekasi, Depok, Pekanbaru, Surabaya, Makassar, Bandung, Jakarta, Medan, Palembang, Semarang, dan Tangerang.

Berikutnya, sebagai contoh, saya ingin melihat trending topic di Bandung. Maka langkah pertamanya adalah dengan memfilter WOEID terlebih dahulu, atau secara spesifik kita pilih WOEID Bandung, dengan script berikut ini:

#filter the data frame for Bandung (Indonesia) and extract the woeid of the same location
LocsIndonesia = subset(Locs, country == "Indonesia")
woeidBandung = subset(LocsIndonesia, name == "Bandung")$woeid

Selanjutnya, kita dapat mendapatkan top trending topic terkini di Bandung, dengan script berikut:

#getTrends takes a specified woeid and returns the trending topics associated with that woeid
trends = getTrends(woeid = woeidBandung)

#print top trends in Bandung, Indonesia
head(trends)

Hasil di R Console, sebagai berikut:

Data Scientist, Data Science, Machine Learning, Statistics, Data Science Indonesia, Data Analytics, Data Analysis, Data Analyst, Data, Astronomy, Astronomer, Science, Python, iPython, Jupyter Notebook, R, RStudio, Excel, Coding, Koding, Cara Mengolah Data, Mengolah Data, Olah Data, Programming, Pemrograman, Sains, Teknologi, Ilmu Data, Teknologi Informasi, Tech in Asia, Teknologi, Technology, Sains, Bisnis, Business, Business Analyst, Business Analysis, Social Media Mining, Movie Review, Muhammad Azizul Hakim, Aziz

Sumber Gambar: Pengalaman Pribadi.

Cuma sedikit ya? Hanya 6 top trending topics yang kita peroleh. Bagaimana jika kita ingin tahu lebih banyak?

Mudah saja, cukup ketikkan trends saja pada console, seperti berikut ini:

Data Scientist, Data Science, Machine Learning, Statistics, Data Science Indonesia, Data Analytics, Data Analysis, Data Analyst, Data, Astronomy, Astronomer, Science, Python, iPython, Jupyter Notebook, R, RStudio, Excel, Coding, Koding, Cara Mengolah Data, Mengolah Data, Olah Data, Programming, Pemrograman, Sains, Teknologi, Ilmu Data, Teknologi Informasi, Tech in Asia, Teknologi, Technology, Sains, Bisnis, Business, Business Analyst, Business Analysis, Social Media Mining, Movie Review, Muhammad Azizul Hakim, Aziz

Sumber Gambar: Pengalaman Pribadi.

Atau hasil lengkapnya, saya lampirkan sebagai berikut:

name url query woeid
1 #HariAnakNasional2018 http://twitter.com/search?q=%23HariAnakNasional2018 %23HariAnakNasional2018 1047180
2 #20thPKBjogja http://twitter.com/search?q=%2320thPKBjogja %2320thPKBjogja 1047180
3 #MenolakLupa http://twitter.com/search?q=%23MenolakLupa %23MenolakLupa 1047180
4 Padi Reborn http://twitter.com/search?q=%22Padi+Reborn%22 %22Padi+Reborn%22 1047180
5 #BantenJKWMenang2019 http://twitter.com/search?q=%23BantenJKWMenang2019 %23BantenJKWMenang2019 1047180
6 #PertaminaEnergiKeluarga http://twitter.com/search?q=%23PertaminaEnergiKeluarga %23PertaminaEnergiKeluarga 1047180
7 Mesut Ozil http://twitter.com/search?q=%22Mesut+Ozil%22 %22Mesut+Ozil%22 1047180
8 Sumatera http://twitter.com/search?q=Sumatera Sumatera 1047180
9 Kalapas Sukamiskin Wahid Husen http://twitter.com/search?q=%22Kalapas+Sukamiskin+Wahid+Husen%22 %22Kalapas+Sukamiskin+Wahid+Husen%22 1047180
10 Agus Harimurti Yudhoyono http://twitter.com/search?q=%22Agus+Harimurti+Yudhoyono%22 %22Agus+Harimurti+Yudhoyono%22 1047180
11 Capres http://twitter.com/search?q=Capres Capres 1047180
12 Eza Gionino http://twitter.com/search?q=%22Eza+Gionino%22 %22Eza+Gionino%22 1047180
13 Dzuhur http://twitter.com/search?q=Dzuhur Dzuhur 1047180
14 Karius http://twitter.com/search?q=Karius Karius 1047180
15 Cawang http://twitter.com/search?q=Cawang Cawang 1047180
16 Tommy Soeharto http://twitter.com/search?q=%22Tommy+Soeharto%22 %22Tommy+Soeharto%22 1047180
17 Forum http://twitter.com/search?q=Forum Forum 1047180
18 #DivestasiUntukKedaulatan http://twitter.com/search?q=%23DivestasiUntukKedaulatan %23DivestasiUntukKedaulatan 1047180
19 #KemenanganItuDekat http://twitter.com/search?q=%23KemenanganItuDekat %23KemenanganItuDekat 1047180
20 #PesonaAP2dukungPariwisata http://twitter.com/search?q=%23PesonaAP2dukungPariwisata %23PesonaAP2dukungPariwisata 1047180
21 #NasdemJokowi http://twitter.com/search?q=%23NasdemJokowi %23NasdemJokowi 1047180
22 #8YearsOfOneDirection http://twitter.com/search?q=%238YearsOfOneDirection %238YearsOfOneDirection 1047180
23 #DJShow http://twitter.com/search?q=%23DJShow %23DJShow 1047180
24 #TOP10DestinasiTorchRelayBali http://twitter.com/search?q=%23TOP10DestinasiTorchRelayBali %23TOP10DestinasiTorchRelayBali 1047180
25 #TiketKertasKRL http://twitter.com/search?q=%23TiketKertasKRL %23TiketKertasKRL 1047180
26 #ElshintaEdisiSiang http://twitter.com/search?q=%23ElshintaEdisiSiang %23ElshintaEdisiSiang 1047180
27 #RokokHarusMahal http://twitter.com/search?q=%23RokokHarusMahal %23RokokHarusMahal 1047180
28 #Wbssembuh http://twitter.com/search?q=%23Wbssembuh %23Wbssembuh 1047180
29 #infoBDG http://twitter.com/search?q=%23infoBDG %23infoBDG 1047180
30 #CalonPenghuniNeraka http://twitter.com/search?q=%23CalonPenghuniNeraka %23CalonPenghuniNeraka 1047180
31 #Lurikcoffeekitchen http://twitter.com/search?q=%23Lurikcoffeekitchen %23Lurikcoffeekitchen 1047180
32 #HabibDukungJKW2019 http://twitter.com/search?q=%23HabibDukungJKW2019 %23HabibDukungJKW2019 1047180
33 #MenikahItuMudah http://twitter.com/search?q=%23MenikahItuMudah %23MenikahItuMudah 1047180
34 #EKTHARAJAEKTHIRANI01 http://twitter.com/search?q=%23EKTHARAJAEKTHIRANI01 %23EKTHARAJAEKTHIRANI01 1047180
35 #PagiPagi http://twitter.com/search?q=%23PagiPagi %23PagiPagi 1047180
36 #Film22Menit http://twitter.com/search?q=%23Film22Menit %23Film22Menit 1047180
37 #AnakTwitterHarapNgumpul http://twitter.com/search?q=%23AnakTwitterHarapNgumpul %23AnakTwitterHarapNgumpul 1047180
38 #TrisoulsDiPRO2FM http://twitter.com/search?q=%23TrisoulsDiPRO2FM %23TrisoulsDiPRO2FM 1047180
39 #GermanGP http://twitter.com/search?q=%23GermanGP %23GermanGP 1047180
40 #ElshintaNewsAndTalk http://twitter.com/search?q=%23ElshintaNewsAndTalk %23ElshintaNewsAndTalk 1047180
41 #teropongsenayan http://twitter.com/search?q=%23teropongsenayan %23teropongsenayan 1047180
42 #MontessoriMadeSimple http://twitter.com/search?q=%23MontessoriMadeSimple %23MontessoriMadeSimple 1047180
43 #LigaMahasiswa http://twitter.com/search?q=%23LigaMahasiswa %23LigaMahasiswa 1047180
44 #SITM http://twitter.com/search?q=%23SITM %23SITM 1047180
45 #thebestpol http://twitter.com/search?q=%23thebestpol %23thebestpol 1047180
46 #MTVHottest http://twitter.com/search?q=%23MTVHottest %23MTVHottest 1047180
47 #ApiOborAG2018ApiBiruKawahIjen http://twitter.com/search?q=%23ApiOborAG2018ApiBiruKawahIjen %23ApiOborAG2018ApiBiruKawahIjen 1047180
48 #Pemilu2019 http://twitter.com/search?q=%23Pemilu2019 %23Pemilu2019 1047180

Done! Menyenangkan bukan?

Sekarang kita bisa tahu, topik apa yang sedang ramai dibicarakan netizen twitter di suatu daerah. 🙂

Apabila teman-teman ingin mengetahui trending topic dunia, cukup ganti country menjadi Worlwide, ataupun jika teman-teman penasaran dengan trending topic di kota atau negara lain, anda cukup ubah-ubah saja lokasinya (name, country, atau woeid).

Selamat mencoba, enjoy! 😀

 

References & Further Reading

Ravindran et. al (2015): Mastering Social Media Mining with R, Packt Publishing.

WOEIDs in Twitter’s Trends.

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