All language subtitles for 6. Youtube data viewer

af Afrikaans
sq Albanian
am Amharic
ar Arabic Download
hy Armenian
az Azerbaijani
eu Basque
be Belarusian
bn Bengali
bs Bosnian
bg Bulgarian
ca Catalan
ceb Cebuano
ny Chichewa
zh-CN Chinese (Simplified)
zh-TW Chinese (Traditional)
co Corsican
hr Croatian
cs Czech
da Danish
nl Dutch
en English
eo Esperanto
et Estonian
tl Filipino
fi Finnish
fr French
fy Frisian
gl Galician
ka Georgian
de German
el Greek
gu Gujarati
ht Haitian Creole
ha Hausa
haw Hawaiian
iw Hebrew
hi Hindi
hmn Hmong
hu Hungarian
is Icelandic
ig Igbo
id Indonesian
ga Irish
it Italian
ja Japanese
jw Javanese
kn Kannada
kk Kazakh
km Khmer
ko Korean
ku Kurdish (Kurmanji)
ky Kyrgyz
lo Lao
la Latin
lv Latvian
lt Lithuanian
lb Luxembourgish
mk Macedonian
mg Malagasy
ms Malay
ml Malayalam
mt Maltese
mi Maori
mr Marathi
mn Mongolian
my Myanmar (Burmese)
ne Nepali
no Norwegian
ps Pashto
fa Persian
pl Polish
pt Portuguese
pa Punjabi
ro Romanian
ru Russian
sm Samoan
gd Scots Gaelic
sr Serbian
st Sesotho
sn Shona
sd Sindhi
si Sinhala
sk Slovak
sl Slovenian
so Somali
es Spanish
su Sundanese
sw Swahili
sv Swedish
tg Tajik
ta Tamil
te Telugu
th Thai
tr Turkish
uk Ukrainian
ur Urdu
uz Uzbek
vi Vietnamese
cy Welsh
xh Xhosa
yi Yiddish
yo Yoruba
zu Zulu
or Odia (Oriya)
rw Kinyarwanda
tk Turkmen
tt Tatar
ug Uyghur
Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:00,060 --> 00:00:07,770 Look at the YouTube tool from Amnesty International called the YouTube data viewer who found it citizen 2 00:00:08,100 --> 00:00:11,310 evidence that Amnesty USA dot org. 3 00:00:12,310 --> 00:00:19,120 So, as the name implies, it takes certain information from YouTube videos and it pulls additional 4 00:00:19,120 --> 00:00:20,080 information from it. 5 00:00:21,370 --> 00:00:26,920 So really good use case for this would be if we're doing an open investigation, there happens to be 6 00:00:27,740 --> 00:00:28,750 a YouTube video. 7 00:00:29,260 --> 00:00:31,300 Well, how do we go about investigating that video? 8 00:00:31,330 --> 00:00:31,660 Well. 9 00:00:32,910 --> 00:00:38,130 Will typically look at the video or pull it down, will analyze the video, will kind of break apart 10 00:00:38,670 --> 00:00:40,540 certain images that we see in the video. 11 00:00:40,830 --> 00:00:43,110 We might do reverse image search is on it. 12 00:00:43,590 --> 00:00:49,500 Try to pull the data was uploaded, the timestamp and what and any other information. 13 00:00:50,070 --> 00:00:51,690 This makes it a little bit easier. 14 00:00:51,900 --> 00:00:54,300 Again, we could do the same stuff, however. 15 00:00:54,510 --> 00:00:56,400 Let's take a look at how this tool works. 16 00:00:57,420 --> 00:01:04,500 So I have a random YouTube video open here, so I'm just going to copy the YouTube URL here and read 17 00:01:04,500 --> 00:01:05,640 a patient in the tool here. 18 00:01:06,770 --> 00:01:07,940 In way you click go. 19 00:01:09,780 --> 00:01:12,930 So right off the bat, we have some basic information. 20 00:01:12,960 --> 00:01:16,020 We have the video title, of course we have the description in here. 21 00:01:17,050 --> 00:01:24,160 We also have the video I.D., we have the upload date and the upload time and. 22 00:01:25,110 --> 00:01:27,670 Currently, the video was uploaded in UTC. 23 00:01:27,690 --> 00:01:29,950 We can divert this to local time if we want to. 24 00:01:29,970 --> 00:01:31,260 By clicking the link here. 25 00:01:33,220 --> 00:01:34,330 And be a go back here. 26 00:01:34,450 --> 00:01:40,720 Now the great thing about this is it takes thumbnails from the particular video and we see there's four 27 00:01:40,720 --> 00:01:41,680 thumbnails here. 28 00:01:42,760 --> 00:01:45,370 So if I click on reverse image search here. 29 00:01:46,900 --> 00:01:52,000 Then it's going to pull various information here, so if I can, I could just kind of scroll down here. 30 00:01:52,010 --> 00:01:55,930 I don't see any particularly matching pictures here. 31 00:01:58,550 --> 00:02:05,000 This may be later on in the video, but going straight from just the surface level of it, what we see 32 00:02:05,440 --> 00:02:06,680 a way to keep scrolling down here. 33 00:02:06,800 --> 00:02:09,410 Now this is an exact match, the image that we saw. 34 00:02:10,130 --> 00:02:12,890 So we could see there's a couple of YouTube videos here. 35 00:02:16,630 --> 00:02:21,520 And not all of these are the same towns, so these are actually different videos, we may potentially 36 00:02:21,520 --> 00:02:24,430 be able to get more information from these other videos here. 37 00:02:25,990 --> 00:02:31,240 And I can see here that this one here since for Chris is world board, Chris, we're all on Pinterest. 38 00:02:31,240 --> 00:02:36,850 So again, if I'm investigating that particular video for whatever reason, I see that, well, hey, 39 00:02:36,850 --> 00:02:42,550 this video is linked also to Pinterest site so I can go there and potentially get more information about 40 00:02:42,550 --> 00:02:42,700 it. 41 00:02:44,190 --> 00:02:48,280 And if I go back here, we can click on the other images and again, he'll do a reverse image search 42 00:02:48,280 --> 00:02:49,510 wit using Google. 43 00:02:50,900 --> 00:02:56,360 And I can just scroll down here and I can see, well, no, I had some hits that aren't directly tied 44 00:02:56,360 --> 00:02:57,050 to YouTube. 45 00:02:57,950 --> 00:02:59,870 So, again, pretty useful tool. 46 00:03:00,260 --> 00:03:01,280 Really interesting. 47 00:03:02,060 --> 00:03:08,480 It's free, it's all online again, you can find out citizen evidence that Amnesty USA dot org. 48 00:03:09,350 --> 00:03:13,250 And again, this is Amnesty International tool YouTube data viewer. 49 00:03:13,670 --> 00:03:14,540 Thank you for watching. 50 00:03:14,660 --> 00:03:15,590 I'll see you next VIDEO. 4721

Can't find what you're looking for?
Get subtitles in any language from opensubtitles.com, and translate them here.