Online resources related to SOC Analysts. Incident investigation reference material, blogs, newsletters, good reads, books, trainings, podcasts, Twitter/X accounts and a set of tools relevant to the role of SOC analyst.

​ ​#​ ​Ukraine-Cyber-Operations ​Curated Intelligence is working with analysts from around the world to provide useful information to organisations in Ukraine looking for additional free threat intelligence. Slava Ukraini. Glory to Ukraine. ([​Blog​](https://www.curatedintel.org/2021/08/welcome.html) | [​Twitter​](https://twitter.com/CuratedIntel) | [​LinkedIn​](https://www.linkedin.com/company/curatedintelligence/)) ​ ​ ​###​ ​Analyst Comments: ​-​ 2022-02-25 ​  ​-​ Creation of the initial repository to help organisations in Ukraine ​  ​-​ Added [​Threat Reports​](https://github.com/curated-intel/Ukraine-Cyber-Operations#threat-reports) section ​  ​-​ Added [​Vendor Support​](https://github.com/curated-intel/Ukraine-Cyber-Operations#vendor-support) section ​-​ 2022-02-26 ​  ​-​ Additional resources, chronologically ordered (h/t Orange-CD) ​  ​-​ Added [​Vetted OSINT Sources​](https://github.com/curated-intel/Ukraine-Cyber-Operations#vetted-osint-sources) section  ​  ​-​ Added [​Miscellaneous Resources​](https://github.com/curated-intel/Ukraine-Cyber-Operations#miscellaneous-resources) section ​-​ 2022-02-27 ​  ​-​ Additional threat reports have been added ​  ​-​ Added [​Data Brokers​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/README.md#data-brokers) section ​  ​-​ Added [​Access Brokers​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/README.md#access-brokers) section ​-​ 2022-02-28 ​  ​-​ Added Russian Cyber Operations Against Ukraine Timeline by ETAC ​  ​-​ Added Vetted and Contextualized [​Indicators of Compromise (IOCs)​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/ETAC_Vetted_UkraineRussiaWar_IOCs.csv) by ETAC ​-​ 2022-03-01 ​  ​-​ Additional threat reports and resources have been added ​-​ 2022-03-02 ​  ​-​ Additional [​Indicators of Compromise (IOCs)​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/ETAC_Vetted_UkraineRussiaWar_IOCs.csv#L2011) have been added ​  ​-​ Added vetted [​YARA rule collection​](https://github.com/curated-intel/Ukraine-Cyber-Operations/tree/main/yara) from the Threat Reports by ETAC ​  ​-​ Added loosely-vetted [​IOC Threat Hunt Feeds​](https://github.com/curated-intel/Ukraine-Cyber-Operations/tree/main/KPMG-Egyde_Ukraine-Crisis_Feeds/MISP-CSV_MediumConfidence_Filtered) by KPMG-Egyde CTI (h/t [​0xDISREL​](https://twitter.com/0xDISREL)) ​    ​-​ IOCs shared by these feeds are ​`LOW-TO-MEDIUM CONFIDENCE`​ we strongly recommend NOT adding them to a blocklist ​    ​-​ These could potentially be used for ​`THREAT HUNTING`​ and could be added to a ​`WATCHLIST` ​    ​-​ IOCs are generated in ​`MISP COMPATIBLE`​ CSV format ​-​ 2022-03-03 ​  ​-​ Additional threat reports and vendor support resources have been added ​  ​-​ Updated [​Log4Shell IOC Threat Hunt Feeds​](https://github.com/curated-intel/Log4Shell-IOCs/tree/main/KPMG_Log4Shell_Feeds) by KPMG-Egyde CTI; not directly related to Ukraine, but still a widespread vulnerability. ​  ​-​ Added diagram of Russia-Ukraine Cyberwar Participants 2022 by ETAC ​  ​-​ Additional [​Indicators of Compromise (IOCs)​](https://github.com/curated-intel/Ukraine-Cyber-Operations/blob/main/ETAC_Vetted_UkraineRussiaWar_IOCs.csv#L2042) have been added ​####​ ​`Threat Reports` ​| Date | Source | Threat(s) | URL | ​| --- | --- | --- | --- | ​| 14 JAN | SSU Ukraine | Website Defacements | [ssu.gov.ua](https://ssu.gov.ua/novyny/sbu-rozsliduie-prychetnist-rosiiskykh-spetssluzhb-do-sohodnishnoi-kiberataky-na-orhany-derzhavnoi-vlady-ukrainy)| ​| 15 JAN | Microsoft | WhisperGate wiper (DEV-0586) | [microsoft.com](https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/) | ​| 19 JAN | Elastic | WhisperGate wiper (Operation BleedingBear) | [elastic.github.io](https://elastic.github.io/security-research/malware/2022/01/01.operation-bleeding-bear/article/) | ​| 31 JAN | Symantec | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [symantec-enterprise-blogs.security.com](https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/shuckworm-gamaredon-espionage-ukraine) | ​| 2 FEB | RaidForums | Access broker "GodLevel" offering Ukrainain algricultural exchange | RaidForums [not linked] | ​| 2 FEB | CERT-UA | UAC-0056 using SaintBot and OutSteel malware | [cert.gov.ua](https://cert.gov.ua/article/18419) | ​| 3 FEB | PAN Unit42 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [unit42.paloaltonetworks.com](https://unit42.paloaltonetworks.com/gamaredon-primitive-bear-ukraine-update-2021/) | ​| 4 FEB | Microsoft | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [microsoft.com](https://www.microsoft.com/security/blog/2022/02/04/actinium-targets-ukrainian-organizations/) | ​| 8 FEB | NSFOCUS | Lorec53 (aka UAC-0056, EmberBear, BleedingBear) | [nsfocusglobal.com](https://nsfocusglobal.com/apt-retrospection-lorec53-an-active-russian-hack-group-launched-phishing-attacks-against-georgian-government) | ​| 15 FEB | CERT-UA | DDoS attacks against the name server of government websites as well as Oschadbank (State Savings Bank) & Privatbank (largest commercial bank). False SMS and e-mails to create panic | [cert.gov.ua](https://cert.gov.ua/article/37139) | ​| 23 FEB | The Daily Beast | Ukrainian troops receive threatening SMS messages | [thedailybeast.com](https://www.thedailybeast.com/cyberattacks-hit-websites-and-psy-ops-sms-messages-targeting-ukrainians-ramp-up-as-russia-moves-into-ukraine) | ​| 23 FEB | UK NCSC | Sandworm/VoodooBear (GRU) | [ncsc.gov.uk](https://www.ncsc.gov.uk/files/Joint-Sandworm-Advisory.pdf) | ​| 23 FEB | SentinelLabs | HermeticWiper | [sentinelone.com]( https://www.sentinelone.com/labs/hermetic-wiper-ukraine-under-attack/ ) | ​| 24 FEB | ESET | HermeticWiper | [welivesecurity.com](https://www.welivesecurity.com/2022/02/24/hermeticwiper-new-data-wiping-malware-hits-ukraine/) | ​| 24 FEB | Symantec | HermeticWiper, PartyTicket ransomware, CVE-2021-1636, unknown webshell | [symantec-enterprise-blogs.security.com](https://symantec-enterprise-blogs.security.com/blogs/threat-intelligence/ukraine-wiper-malware-russia) | ​| 24 FEB | Cisco Talos | HermeticWiper | [blog.talosintelligence.com](https://blog.talosintelligence.com/2022/02/threat-advisory-hermeticwiper.html) | ​| 24 FEB | Zscaler | HermeticWiper | [zscaler.com](https://www.zscaler.com/blogs/security-research/hermetic-wiper-resurgence-targeted-attacks-ukraine) | ​| 24 FEB | Cluster25 | HermeticWiper | [cluster25.io](https://cluster25.io/2022/02/24/ukraine-analysis-of-the-new-disk-wiping-malware/) | ​| 24 FEB | CronUp | Data broker "FreeCivilian" offering multiple .gov.ua | [twitter.com/1ZRR4H](https://twitter.com/1ZRR4H/status/1496931721052311557)| ​| 24 FEB | RaidForums | Data broker "Featherine" offering diia.gov.ua | RaidForums [not linked] | ​| 24 FEB | DomainTools | Unknown scammers | [twitter.com/SecuritySnacks](https://twitter.com/SecuritySnacks/status/1496956492636905473?s=20&t=KCIX_1Ughc2Fs6Du-Av0Xw) | ​| 25 FEB | @500mk500 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [twitter.com/500mk500](https://twitter.com/500mk500/status/1497339266329894920?s=20&t=opOtwpn82ztiFtwUbLkm9Q) | ​| 25 FEB | @500mk500 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [twitter.com/500mk500](https://twitter.com/500mk500/status/1497208285472215042)| ​| 25 FEB | Microsoft | HermeticWiper | [gist.github.com](https://gist.github.com/fr0gger/7882fde2b1b271f9e886a4a9b6fb6b7f) | ​| 25 FEB | 360 NetLab | DDoS (Mirai, Gafgyt, IRCbot, Ripprbot, Moobot) | [blog.netlab.360.com](https://blog.netlab.360.com/some_details_of_the_ddos_attacks_targeting_ukraine_and_russia_in_recent_days/) | ​| 25 FEB | Conti [themselves] | Conti ransomware, BazarLoader | Conti News .onion [not linked] | ​| 25 FEB | CoomingProject [themselves] | Data Hostage Group | CoomingProject Telegram [not linked] | ​| 25 FEB | CERT-UA | UNC1151/Ghostwriter (Belarus MoD) | [CERT-UA Facebook](https://facebook.com/story.php?story_fbid=312939130865352&id=100064478028712)| ​| 25 FEB | Sekoia | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com/sekoia_io](https://twitter.com/sekoia_io/status/1497239319295279106) | ​| 25 FEB | @jaimeblascob | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com/jaimeblasco](https://twitter.com/jaimeblascob/status/1497242668627370009)| ​| 25 FEB | RISKIQ | UNC1151/Ghostwriter (Belarus MoD) | [community.riskiq.com](https://community.riskiq.com/article/e3a7ceea/) | ​| 25 FEB | MalwareHunterTeam | Unknown phishing | [twitter.com/malwrhunterteam](https://twitter.com/malwrhunterteam/status/1497235270416097287) | ​| 25 FEB | ESET | Unknown scammers | [twitter.com/ESETresearch](https://twitter.com/ESETresearch/status/1497194165561659394) | ​| 25 FEB | BitDefender | Unknown scammers | [blog.bitdefender.com](https://blog.bitdefender.com/blog/hotforsecurity/cybercriminals-deploy-spam-campaign-as-tens-of-thousands-of-ukrainians-seek-refuge-in-neighboring-countries/) | ​| 25 FEB | SSSCIP Ukraine | Unkown phishing | [twitter.com/dsszzi](https://twitter.com/dsszzi/status/1497103078029291522) | ​| 25 FEB | RaidForums | Data broker "NetSec"  offering FSB (likely SMTP accounts) | RaidForums [not linked] | ​| 25 FEB | Zscaler | PartyTicket decoy ransomware | [zscaler.com](https://www.zscaler.com/blogs/security-research/technical-analysis-partyticket-ransomware) | ​| 25 FEB | INCERT GIE | Cyclops Blink, HermeticWiper | [linkedin.com](https://www.linkedin.com/posts/activity-6902989337210740736-XohK) [Login Required] | ​| 25 FEB | Proofpoint | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com/threatinsight](https://twitter.com/threatinsight/status/1497355737844133895?s=20&t=Ubi0tb_XxGCbHLnUoQVp8w) | ​| 25 FEB | @fr0gger_ | HermeticWiper capabilities Overview | [twitter.com/fr0gger_](https://twitter.com/fr0gger_/status/1497121876870832128?s=20&t=_296n0bPeUgdXleX02M9mg) ​| 26 FEB | BBC Journalist | A fake Telegram account claiming to be President Zelensky is posting dubious messages | [twitter.com/shayan86](https://twitter.com/shayan86/status/1497485340738785283?s=21) | ​| 26 FEB | CERT-UA | UNC1151/Ghostwriter (Belarus MoD) | [CERT_UA Facebook](https://facebook.com/story.php?story_fbid=313517477474184&id=100064478028712) | ​| 26 FEB | MHT and TRMLabs | Unknown scammers, linked to ransomware | [twitter.com/joes_mcgill](https://twitter.com/joes_mcgill/status/1497609555856932864?s=20&t=KCIX_1Ughc2Fs6Du-Av0Xw) | ​| 26 FEB | US CISA | WhisperGate wiper, HermeticWiper | [cisa.gov](https://www.cisa.gov/uscert/ncas/alerts/aa22-057a) | ​| 26 FEB | Bloomberg | Destructive malware (possibly HermeticWiper) deployed at Ukrainian Ministry of Internal Affairs & data stolen from Ukrainian telecommunications networks | [bloomberg.com](https://www.bloomberg.com/news/articles/2022-02-26/hackers-destroyed-data-at-key-ukraine-agency-before-invasion?sref=ylv224K8) | ​| 26 FEB | Vice Prime Minister of Ukraine | IT ARMY of Ukraine created to crowdsource offensive operations against Russian infrastructure | [twitter.com/FedorovMykhailo](https://twitter.com/FedorovMykhailo/status/1497642156076511233) | ​| 26 FEB | Yoroi | HermeticWiper | [yoroi.company](https://yoroi.company/research/diskkill-hermeticwiper-a-disruptive-cyber-weapon-targeting-ukraines-critical-infrastructures) | ​| 27 FEB | LockBit [themselves] | LockBit ransomware | LockBit .onion [not linked] |  ​| 27 FEB | ALPHV [themselves] | ALPHV ransomware | vHUMINT [closed source] | ​| 27 FEB | Mēris Botnet [themselves] | DDoS attacks | vHUMINT [closed source] | ​| 28 FEB | Horizon News [themselves] | Leak of China's Censorship Order about Ukraine | [TechARP](https://www-techarp-com.cdn.ampproject.org/c/s/www.techarp.com/internet/chinese-media-leaks-ukraine-censor/?amp=1)| ​| 28 FEB | Microsoft | FoxBlade (aka HermeticWiper) | [Microsoft](https://blogs.microsoft.com/on-the-issues/2022/02/28/ukraine-russia-digital-war-cyberattacks/?preview_id=65075) | ​| 28 FEB | @heymingwei | Potential BGP hijacks attempts against Ukrainian Internet Names Center | [https://twitter.com/heymingwei](https://twitter.com/heymingwei/status/1498362715198263300?s=20&t=Ju31gTurYc8Aq_yZMbvbxg) | ​| 28 FEB | @cyberknow20 | Stormous ransomware targets Ukraine Ministry of Foreign Affairs | [twitter.com/cyberknow20](https://twitter.com/cyberknow20/status/1498434090206314498?s=21) |  ​| 1 MAR | ESET | IsaacWiper and HermeticWizard | [welivesecurity.com](https://www.welivesecurity.com/2022/03/01/isaacwiper-hermeticwizard-wiper-worm-targeting-ukraine/) | ​| 1 MAR | Proofpoint | Ukrainian armed service member's email compromised and sent malspam containing the SunSeed malware (likely TA445/UNC1151/Ghostwriter) | [proofpoint.com](https://www.proofpoint.com/us/blog/threat-insight/asylum-ambuscade-state-actor-uses-compromised-private-ukrainian-military-emails) | ​| 1 MAR | Elastic | HermeticWiper | [elastic.github.io](https://elastic.github.io/security-research/intelligence/2022/03/01.hermeticwiper-targets-ukraine/article/) | ​| 1 MAR | CrowdStrike | PartyTicket (aka HermeticRansom), DriveSlayer (aka HermeticWiper) | [CrowdStrike](https://www.crowdstrike.com/blog/how-to-decrypt-the-partyticket-ransomware-targeting-ukraine/) | ​| 2 MAR | Zscaler | DanaBot operators launch DDoS attacks against the Ukrainian Ministry of Defense | [zscaler.com](https://www.zscaler.com/blogs/security-research/danabot-launches-ddos-attack-against-ukrainian-ministry-defense) | ​| 3 MAR | @ShadowChasing1 | Gamaredon/Shuckworm/PrimitiveBear (FSB) | [twitter.com/ShadowChasing1](https://twitter.com/ShadowChasing1/status/1499361093059153921) | ​| 3 MAR | @vxunderground | News website in Poland was reportedly compromised and the threat actor uploaded anti-Ukrainian propaganda | [twitter.com/vxunderground](https://twitter.com/vxunderground/status/1499374914758918151?s=20&t=jyy9Hnpzy-5P1gcx19bvIA) | ​| 3 MAR | @kylaintheburgh | Russian botnet on Twitter is pushing "#istandwithputin" and "#istandwithrussia" propaganda (in English) | [twitter.com/kylaintheburgh](https://twitter.com/kylaintheburgh/status/1499350578371067906?s=21) | ​| 3 MAR | @tracerspiff | UNC1151/Ghostwriter (Belarus MoD) | [twitter.com](https://twitter.com/tracerspiff/status/1499444876810854408?s=21) | ​####​ ​`Access Brokers` ​| Date | Threat(s) | Source | ​| --- | --- | --- | ​| 23 JAN | Access broker "Mont4na" offering UkrFerry | RaidForums [not linked] | ​| 23 JAN | Access broker "Mont4na" offering PrivatBank | RaidForums [not linked] | ​| 24 JAN | Access broker "Mont4na" offering DTEK | RaidForums [not linked] | ​| 27 FEB | KelvinSecurity Sharing list of IP cameras in Ukraine | vHUMINT [closed source] | ​| 28 FEB | "w1nte4mute" looking to buy access to UA and NATO countries (likely ransomware affiliate) | vHUMINT [closed source] | ​####​ ​`Data Brokers` ​| Threat Actor    | Type            | Observation                                                                                               | Validated | Relevance                     | Source                                                     | ​| --------------- | --------------- | --------------------------------------------------------------------------------------------------------- | --------- | ----------------------------- | ---------------------------------------------------------- | ​| aguyinachair    | UA data sharing | PII DB of ukraine.com (shared as part of a generic compilation)                                           | No        | TA discussion in past 90 days | ELeaks Forum \[not linked\]                                | ​| an3key          | UA data sharing | DB of Ministry of Communities and Territories Development of Ukraine (minregion\[.\]gov\[.\]ua)           | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| an3key          | UA data sharing | DB of Ukrainian Ministry of Internal Affairs (wanted\[.\]mvs\[.\]gov\[.\]ua)                              | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (40M) of PrivatBank customers (privatbank\[.\]ua)                                                  | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | DB of "border crossing" DBs of DPR and LPR                                                                | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (7.5M) of Ukrainian passports                                                                      | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB of Ukrainian car registration, license plates, Ukrainian traffic police records                    | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (2.1M) of Ukrainian citizens                                                                       | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (28M) of Ukrainian citizens (passports, drivers licenses, photos)                                  | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (1M) of Ukrainian postal/courier service customers (novaposhta\[.\]ua)                             | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (10M) of Ukrainian telecom customers (vodafone\[.\]ua)                                             | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (3M) of Ukrainian telecom customers (lifecell\[.\]ua)                                              | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| CorelDraw       | UA data sharing | PII DB (13M) of Ukrainian telecom customers (kyivstar\[.\]ua)                                             | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| danieltx51      | UA data sharing | DB of Ministry of Foreign Affairs of Ukraine (mfa\[.\]gov\[.\]ua)                                         | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| DueDiligenceCIS | UA data sharing | PII DB (63M) of Ukrainian citizens (name, DOB, birth country, phone, TIN, passport, family, etc)          | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Featherine      | UA data sharing | DB of Ukrainian 'Diia' e-Governance Portal for Ministry of Digital Transformation of Ukraine              | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of Ministry for Internal Affairs of Ukraine public data search engine (wanted\[.\]mvs\[.\]gov\[.\]ua)  | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of Ministry for Communities and Territories Development of Ukraine (minregion\[.\]gov\[.\]ua)          | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of Motor Insurance Bureau of Ukraine (mtsbu\[.\]ua)                                                    | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | PII DB of Ukrainian digital-medicine provider (medstar\[.\]ua)                                            | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| FreeCivilian    | UA data sharing | DB of ticket.kyivcity.gov.ua                                                                              | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of id.kyivcity.gov.ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of my.kyivcity.gov.ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of portal.kyivcity.gov.ua                                                                              | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of anti-violence-map.msp.gov.ua                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dopomoga.msp.gov.ua                                                                                 | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of e-services.msp.gov.ua                                                                               | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of edu.msp.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of education.msp.gov.ua                                                                                | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of ek-cbi.msp.gov.ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mail.msp.gov.ua                                                                                     | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of portal-gromady.msp.gov.ua                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of web-minsoc.msp.gov.ua                                                                               | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of wcs-wim.dsbt.gov.ua                                                                                 | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of bdr.mvs.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of motorsich.com                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dsns.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mon.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of minagro.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of zt.gov.ua                                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of kmu.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mvs.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dsbt.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of forest.gov.ua                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of nkrzi.gov.ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dabi.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of comin.gov.ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dp.dpss.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of esbu.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mms.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mova.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mspu.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of nads.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of reintegration.gov.ua                                                                                | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of sies.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of sport.gov.ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mepr.gov.ua                                                                                         | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mfa.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of va.gov.ua                                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mtu.gov.ua                                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of cg.mvs.gov.ua                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of ch-tmo.mvs.gov.ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of cp.mvs.gov.ua                                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of cpd.mvs.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of hutirvilnij-mrc.mvs.gov.ua                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dndekc.mvs.gov.ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of visnyk.dndekc.mvs.gov.ua                                                                            | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of dpvs.hsc.gov.ua                                                                                     | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of odk.mvs.gov.ua                                                                                      | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of e-driver\[.\]hsc\[.\]gov\[.\]ua                                                                     | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of wanted\[.\]mvs\[.\]gov\[.\]ua                                                                       | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of minregeion\[.\]gov\[.\]ua                                                                           | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of health\[.\]mia\[.\]solutions                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mtsbu\[.\]ua                                                                                        | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of motorsich\[.\]com                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of kyivcity\[.\]com                                                                                    | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of bdr\[.\]mvs\[.\]gov\[.\]ua                                                                          | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of gkh\[.\]in\[.\]ua                                                                                   | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of kmu\[.\]gov\[.\]ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mon\[.\]gov\[.\]ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of minagro\[.\]gov\[.\]ua                                                                              | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| FreeCivilian    | UA data sharing | DB of mfa\[.\]gov\[.\]ua                                                                                  | No        | TA discussion in past 90 days | FreeCivilian .onion \[not linked\]                         | ​| Intel\_Data     | UA data sharing | PII DB (56M) of Ukrainian Citizens                                                                        | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Kristina        | UA data sharing | DB of Ukrainian National Police (mvs\[.\]gov\[.\]ua)                                                      | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| NetSec          | UA data sharing | PII DB (53M) of Ukrainian citizens                                                                        | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Psycho\_Killer  | UA data sharing | PII DB (56M) of Ukrainian Citizens                                                                        | No        | TA discussion in past 90 days | Exploit Forum .onion \[not linked\]                        | ​| Sp333           | UA data sharing | PII DB of Ukrainian and Russian interpreters, translators, and tour guides                                | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Vaticano        | UA data sharing | DB of Ukrainian 'Diia' e-Governance Portal for Ministry of Digital Transformation of Ukraine \[copy\]     | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​| Vaticano        | UA data sharing | DB of Ministry for Communities and Territories Development of Ukraine (minregion\[.\]gov\[.\]ua) \[copy\] | No        | TA discussion in past 90 days | RaidForums \[not linked; site hijacked since UA invasion\] | ​####​ ​`Vendor Support` ​| Vendor | Offering | URL | ​| --- | --- | --- | ​| Dragos | Access to Dragos service if from US/UK/ANZ and in need of ICS cybersecurity support | [twitter.com/RobertMLee](https://twitter.com/RobertMLee/status/1496862093588455429) | 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​| Orange | IOCs related to Russia-Ukraine 2022 conflict extracted from our Datalake Threat Intelligence platform. | [github.com/Orange-Cyberdefense](https://github.com/Orange-Cyberdefense/russia-ukraine_IOCs)| ​| FSecure | F-Secure FREEDOME VPN is now available for free in all of Ukraine | [twitter.com/FSecure](https://twitter.com/FSecure/status/1497248407303462960) | ​| Multiple vendors | List of vendors offering their services to Ukraine for free, put together by [@chrisculling](https://twitter.com/chrisculling/status/1497023038323404803) | [docs.google.com/spreadsheets](https://docs.google.com/spreadsheets/d/18WYY9p1_DLwB6dnXoiiOAoWYD8X0voXtoDl_ZQzjzUQ/edit#gid=0) | ​| Mandiant | Free threat intelligence, webinar and guidance for defensive measures relevant to the situation in Ukraine. | [mandiant.com](https://www.mandiant.com/resources/insights/ukraine-crisis-resource-center) | ​| Starlink | Satellite internet constellation operated by SpaceX providing satellite Internet access coverage to Ukraine | [twitter.com/elonmusk](https://twitter.com/elonmusk/status/1497701484003213317) | ​| Romania DNSC | Romania’s DNSC – in partnership with Bitdefender – will provide technical consulting, threat intelligence and, free of charge, cybersecurity technology to any business, government institution or private citizen of Ukraine for as long as it is necessary. | [Romania's DNSC Press Release](https://dnsc.ro/citeste/press-release-dnsc-and-bitdefender-work-together-in-support-of-ukraine)| ​| BitDefender | Access to Bitdefender technical consulting, threat intelligence and both consumer and enterprise cybersecurity technology | [bitdefender.com/ukraine/](https://www.bitdefender.com/ukraine/) | ​| NameCheap | Free anonymous hosting and domain name registration to any anti-Putin anti-regime and protest websites for anyone located within Russia and Belarus | [twitter.com/Namecheap](https://twitter.com/Namecheap/status/1498998414020861953) | ​| Avast | Free decryptor for PartyTicket ransomware | [decoded.avast.io](https://decoded.avast.io/threatresearch/help-for-ukraine-free-decryptor-for-hermeticransom-ransomware/) |  ​####​ ​`Vetted OSINT Sources` ​| Handle | Affiliation | ​| --- | --- | ​| [@KyivIndependent](https://twitter.com/KyivIndependent) | English-language journalism in Ukraine | ​| [@IAPonomarenko](https://twitter.com/IAPonomarenko) | Defense reporter with The Kyiv Independent | ​| [@KyivPost](https://twitter.com/KyivPost) | English-language journalism in Ukraine | ​| [@Shayan86](https://twitter.com/Shayan86) | BBC World News Disinformation journalist | ​| [@Liveuamap](https://twitter.com/Liveuamap) | Live Universal Awareness Map (“Liveuamap”) independent global news and information site | ​| [@DAlperovitch](https://twitter.com/DAlperovitch) | The Alperovitch Institute for Cybersecurity Studies, Founder & Former CTO of CrowdStrike | ​| [@COUPSURE](https://twitter.com/COUPSURE) | OSINT investigator for Centre for Information Resilience | ​| [@netblocks](https://twitter.com/netblocks) | London-based Internet's Observatory | ​####​ ​`Miscellaneous Resources` ​| Source | URL | Content | ​| --- | --- | --- | ​| PowerOutages.com | https://poweroutage.com/ua | Tracking PowerOutages across Ukraine | ​| Monash IP Observatory | https://twitter.com/IP_Observatory | Tracking IP address outages across Ukraine | ​| Project Owl Discord | https://discord.com/invite/projectowl | Tracking foreign policy, geopolitical events, military and governments, using a Discord-based crowdsourced approach, with a current emphasis on Ukraine and Russia | ​| russianwarchatter.info | https://www.russianwarchatter.info/ | Known Russian Military Radio Frequencies |

Udacity Data Analyst Nanodegree Project 7 - Wrangle and Analyze WeRateDogs Twitter account.

This is my second project in my Udacity Data Analyst Nanodegree Program. I scrapped and wrangled the Twitter Date of a Twitter user; @WeRateDogs.

Wrangling and analyzing data project for Udacity's Data Analyst Nanodegree. Wrangles WeRateDogs™ (@dog_rates) Twitter data from local, online, and Twitter API sources.

This is Udacity's Data Analyst Nanodegree's 5th project; which is Wrangling and Analyzing WeRateDogs Twitter account.

SocialProfiler is a lightweight OSINT tool that profiles a person using their username or email. It searches platforms like Instagram, Twitter, GitHub, and Facebook to find public profiles and generates an HTML report. Ideal for ethical hackers, investigators, and cybersecurity analysts for quick profiling.

There are several factors which affect the price of a stock. Some of them are daily news articles, volume of that stock traded, sentiment in the market, profit of the company etc. Due to the advancement in technology a large amount of data about the stocks is generated every day in the form of news articles, analyst reviews, twitter data etc. The increasing amount of data is making it increasingly difficult to manually analyse the data to make strategic decisions. We implemented and compared the results of three classification algorithms (1) Naïve Bayes (2) J48 (3) Random Forest

Machine Vision Systems to 2025 by Type (Smart Machine Vision Systems, PC-Based Machine Vision Systems and 3D Machine Vision Systems), Components (Cameras, Frame Grabbers, Processors, Illuminations & Optics, Vision Software and Others) and End-users (Automotive, Consumer Electronics, Food & Beverage, Pharmaceuticals, Logistics and Others) – Global Analysis and Forecast Request A Sample copy of Machine Vision Systems @ https://www.bharatbook.com/request-sample/910972 Machine Vision Systems Market to 2025 – Global Analysis and Forecast by Type, Components, and End-user Industry, machine vision systems market is expected to grow US$ 14.48 billion by 2025 from US$ 7.50 billion in 2015. Machine vision systems can perform complex repetitive tasks with higher accuracy and consistency. Machine vision systems include components such as image sensors, processors, PLC, frame grabbers and more, which are driven by a software package to execute user defined applications. Machine vision systems are also employed in non-inspection applications such as guiding robots, pick and place the parts, dispensing liquids and many more. Key trend which will predominantly impacts the market in coming year is emergence of Industrial IoT (IIoT) or Industry 4.0. IIoT connects information technology with production technology, hence involving widespread analytics and data capture to frequently optimize the processes of factories. Machine vision is one of the most critical and basic technologies to provide IIoT with information. Manufacturing’s rapid amendment of IIoT has led to a renaissance in robotics and the renewed need for machine vision. Moreover, the conventional manufacturing systems are anticipated to renovate owing to the implementation of smart IoT technologies throughout the manufacturing operations. Also, investments in machine vision systems are known to perfectly fit in the vision of future manufacturing for automated inspection and quality management application. The global machine vision systems market for the end-user industries is fragmented into Automotive, Consumer Electronics, Food & Beverage, Pharmaceuticals, Logistics and Others. The segmentation is based upon need for machine vision systems to improve mobility and security. Consumer electronics in the machine vision systems market acquires the majority share, followed by automotive and food & beverages. Short product lifecycles of the consumer electronics products, high quality standards requirements by consumers and high labor investments have resulted in the increasing adoptions of machine visions systems by consumer electronics manufacturers worldwide. The overall market size has been derived using both primary and secondary source. The research process begins with an exhaustive secondary research using internal and external sources to obtain qualitative and quantitative information related to the market. Also, primary interview were conducted with industry participants and commentators in order to validate data and analysis. The participants who typically take part in such a process include industry expert such as VPs, business development managers, market intelligence managers and national sales managers, and external consultant such as valuation experts, research analysts and key opinion leaders specializing in the machine vision systems industry. To Browse the Entire Report, Visit: https://www.bharatbook.com/industrial-goods-machinery-market-research-reports-910972/machine-vision-systems-global-analysis-components-end-users.html Table of Contents 1.1 List of Tables 1.2 List of Figures 2 Introduction 2.1 The Insight Partners Research Report Guidance 3 Key Takeaways 4 Machine Vision Systems Market Landscape 4.1 Overview 4.2 Market Segmentation 4.2.1 Global Machine Vision Systems Market – By Types 4.2.2 Global Machine Vision Systems Market – By Components 4.2.3 Global Machine Vision Systems Market – By End-users 4.2.4 Global Machine Vision Systems Market – By Geography 4.3 Value Chain About Bharat Book Bureau: Bharat Book Bureau is the leading market research information provider for market research reports, company profiles, industry study, country reports, business reports, newsletters and online databases Bharat Book Bureau provides over a million reports from more than 400 publishers around the globe. We cover sectors starting from Aeronautics to Zoology. Contact us at: Bharat Book Bureau Tel: +91 22 27810772 / 27810773 Email: poonam@bharatbook.com Website: www.bharatbook.com Follow us on : Twitter|Facebook| Linkedin |Google Plus

The Social Media Analytics Dashboard is a full-stack app that tracks Instagram, Twitter, Facebook. It combines AI-powered sentiment analysis with real-time metrics, keyword tracking, and visual charts, helping marketers and analysts measure content performance, understand audience sentiment, and optimize campaigns.

Welcome to the Open Government Platform! You'll find here a growing set of open source, open government platform code that allows any city, organization, or government to create an open data site. Below you'll see the first code for the Website, its CMS, Data Management System (DMS) and Visitor Relationship Management (VRM). The Open Government Platform (OGPL) is a joint product from India and the United States to promote transparency and greater citizen engagement by making more government data, documents, tools, and processes publicly available through a freely available, open source platform. By making this available in useful machine-readable formats it allows developers, analysts, media, and academia to develop new applications and insights that will help give citizens more information for better decisions. In using an open source method of development, the OGPL community will provide future technology enhancements, open government solutions, and community-based technical support. OGPL has become an example of a new era of diplomatic collaborations that benefit the global community that promote government transparency, citizen-focused applications, and enrich humanity. The code below is the alpha code base for an OGPL instance. We are currently seeking pre-alpha testers and experienced open source developers to provide recommendations as additional modules are released to the open source community. If you are interested in being part of OGPL, please contact us on the Contact Us page at http://www.opengovplatform.org or on Twitter at @OGPL.

Twitter is one of the most common platforms that people share their ideas about any topic. Also, Twitter provides huge database for researchers and analysts and also for companies. However, this infinite data crates some difficulties. The most important difficulty is that there is huge amount of text data, therefore you need to analyze the results and develop methods for the evaluation. This disadvantage contributes to make long time for analyzing data. Sentiment analysis is that natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. Sentiment analysis is the most common technique for natural language processing in order to understand human languages. In spite of the fact that sentiment analysis has many sections, we interest on “Sentiment Prediction” by classifying the words as “positive”, “negative” or “neutral”. This is the main aim of our project.

Harvesting tweets by hashtag to perform sentiment analysis with IDOL OnDemand platform and store it into VerticaDB.

A mini group project of the course Data Mining and Knowledge Discovery

No description available

A desktop app for collecting, analyzing, and visualizing data from X (Twitter) and Instagram. Ideal for sentiment analysis, trend detection, and insights for marketers, researchers, and analysts.

This is the Release of Beta Code base of the Open Government Platform (OGPL), which is a growing set of open source code that allows any city, organization or government to create an open data site. Below you'll see the code base for the Data Portal, its CMS, Data Management System (DMS) and Visitor Relationship Management (VRM). The Open Government Platform (OGPL) is a joint product from India and the United States to promote transparency and greater citizen engagement by making more government data, documents, tools, and processes publicly available through a freely available, open source platform. By making this available in useful machine-readable formats it allows developers, analysts, media and academia to develop new applications and insights that will help give citizens more information for better decisions. In using an open source method of development, the OGPL community will provide future technology enhancements, open government solutions, and community-based technical support. OGPL has become an example of a new era of diplomatic collaborations that benefit the global community that promote government transparency, citizen-focused applications, and enrich humanity. The code below is the alpha code base for an OGPL instance. We are currently seeking pre-alpha testers and experienced open source developers to provide recommendations as additional modules are released to the open source community. If you are interested in being part of OGPL, please contact us at opengovplatform.org or on Twitter at @OGPL Changes and enhancements incorporate in this release can be seen in the Readme.md file below.

This report is a part of the Wrangle and Analyze data project in the Data Analyst Nanodegree offered by Udacity. In this project, the aim is to gather data about WeRateDogs® from multiple sources including; Twitter API, to clean the data, analyze it, and generate insights

WeRateDogs Project Report Introduction: The below report which made for the Udacity Data Analyst Nanodegree Program of project “WeRateDogs”, I’ll try top explain the process in which my report has gone through. The goal of this project is to practice the process of wrangling and cleaning data, which was made through this twitter account tweet data. Tweets went through a process in which I performed the following activites: - Gathering Data - Assessing Data - Cleaning Data - Gathering Data I this process data is being obtained from csv files and loaded to tables in which it will go through the wrangling process. - Twitter archive data was loaded to `twitter_archive` table whcih contains WeRateDogs Twitter archive, which was provided by the Course and data was imported into the dataframe. - Image prediction data was imported from Image prediction file provided by the course and hosted in Udacity’s servers and added data to `predictions` table. The tweet image predictions, basically predicts whether the object in a said image is a dog or other object. - API data was provided through a file in the course material as my twitter developer account wasn’t created when I’ve started to work on the project, in this file I was able to query API data in JOSN file to read twitter data to api_df_now table. - Assessing Data In this step data is being assessed visually and programmatically to detect quality and tidiness issues in the gathered data. - ‘twitter_archive’ has missing data in multiple tables example, "in_reply_to_status_id", "in_reply_to_user_id", "retweeted_status_id", "in_reply_to_user_id", "retweeted_status_id", "retweeted_status_user_id". Lower case dog names was an issue too. - Another issue is the dog names that can make confusion like doggo, pupper, floofer and puppo. - Timestamp is another issue that needs an attention. Source of content needs to be organized. - Rating values needed some changes. - image predictions columns was making a confsuion. - ‘api_df_now’ file is separate from Twitter archive data. Cleaning Data In this step data is being cleaned and added to new tables twitter_archive_clean, prediction_clean and api_df_now_clean according to the issues observed in after assessing the data. 1- Fixing Quality issues 1- Dropped unnecessary columns containing missing data “in_reply_to_status_id", "in_reply_to_user_id", "retweeted_status_id", "in_reply_to_user_id", "retweeted_status_id", "retweeted_status_user_id". 2- Replaced missing “None” values with “NaN”. 3- Joined “api_df_now” table with “twitter_archive” table and renaming ‘tweet_id’ column. 4- Combined all dog names; doggo, pupper, floofer and puppo under one column name ‘dog’ 5- Changed timestamp to datetime. 6- Optimized source of content: Twitter for iphone, Vine - Make a Scene, Twitter Web Client and TweetDeck. 7- Made a default value for numerator and denominator values. 8- Capitalized first letters of dogs names. 2- Tidiness 1- Changed Image predictions p1, p2 and p3 names to potential_dog1, potential_dog2 and potential_dog3. 2- Merged the cleaned data into the clean tables. Conclusion Through this project I’ve learned to express the data analysis process through code and different tools offered through the Jupyter lab application. Data wrangling is crucial in the data analysis process as it’s the only way top obtain a reliable data to take the proper decisions in any organization. And using python in the process made it much easier and more efficient, also, the different libraries used in the process allowed the data to be read and manipulated in a relatively easier way, which will facilitates the process if dealt with much larger data amounts like Big Data. This proves that using code is a way to manipulate data and alter it efficiently. I believe that through my learning process I’ll be able to dig deeper into more processes and tools which will make the process more fruitful and efficient.

Digital images form a major part of the content on social media and else where. With growing demand for smart-phones havving good cameras and easily accessible editing tools, billions of printed text images are generaated per minute. Significant number of tools have been developed to recognize and extract text from images. Online social media (OSM ) is populated with billions of images comprising memes every day. Images not only capture the essence of a post, but also engage a greater number of viewers than text posts. The core focus of our study was to explore automated image analysis techniques and deep neural network to understand image content posted on social networks on a large scale for which a good text recognitioon tool is required. The state-of-the-art open-source tools show surprisingly inaccurate results on memes and user generated visual contennt on OSM. Hence, a promising open-source OCR is required for assisting researchers and analysts dealing with OSM images on a large scale. Significant research has been done on analyzing the text in posts on social media but little has been done regarding the content delivered by images on social media. Both the text from the posts as well as the images may convey different information to the user. We observed that textual content embedded in images was of opposite polarity to the text contained in posts depicted negative sentiment which makes the textual content of the image so important for study. As per the Wikipedia definition - "Optical character recognition (optical character reader, OCR) is the mechanical or electronic conversion of images of typed, handwritten or printed text into machine- encoded text, whether from a scanned document, a photo of a document, a scene-photo(for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example from a television broadcast)[5]." Our Project specifically targets the memes posted on social media. Memes are the new form of social media marketing tools and are posted widely on social medias like Instagram, Facebook,Whisper, Reddit and Twitter.

How to share data with a statistician This is a guide for anyone who needs to share data with a statistician or data scientist. The target audiences I have in mind are: Collaborators who need statisticians or data scientists to analyze data for them Students or postdocs in various disciplines looking for consulting advice Junior statistics students whose job it is to collate/clean/wrangle data sets The goals of this guide are to provide some instruction on the best way to share data to avoid the most common pitfalls and sources of delay in the transition from data collection to data analysis. The Leek group works with a large number of collaborators and the number one source of variation in the speed to results is the status of the data when they arrive at the Leek group. Based on my conversations with other statisticians this is true nearly universally. My strong feeling is that statisticians should be able to handle the data in whatever state they arrive. It is important to see the raw data, understand the steps in the processing pipeline, and be able to incorporate hidden sources of variability in one's data analysis. On the other hand, for many data types, the processing steps are well documented and standardized. So the work of converting the data from raw form to directly analyzable form can be performed before calling on a statistician. This can dramatically speed the turnaround time, since the statistician doesn't have to work through all the pre-processing steps first. What you should deliver to the statistician To facilitate the most efficient and timely analysis this is the information you should pass to a statistician: The raw data. A tidy data set A code book describing each variable and its values in the tidy data set. An explicit and exact recipe you used to go from 1 -> 2,3 Let's look at each part of the data package you will transfer. The raw data It is critical that you include the rawest form of the data that you have access to. This ensures that data provenance can be maintained throughout the workflow. Here are some examples of the raw form of data: The strange binary file your measurement machine spits out The unformatted Excel file with 10 worksheets the company you contracted with sent you The complicated JSON data you got from scraping the Twitter API The hand-entered numbers you collected looking through a microscope You know the raw data are in the right format if you: Ran no software on the data Did not modify any of the data values You did not remove any data from the data set You did not summarize the data in any way If you made any modifications of the raw data it is not the raw form of the data. Reporting modified data as raw data is a very common way to slow down the analysis process, since the analyst will often have to do a forensic study of your data to figure out why the raw data looks weird. (Also imagine what would happen if new data arrived?) The tidy data set The general principles of tidy data are laid out by Hadley Wickham in this paper and this video. While both the paper and the video describe tidy data using R, the principles are more generally applicable: Each variable you measure should be in one column Each different observation of that variable should be in a different row There should be one table for each "kind" of variable If you have multiple tables, they should include a column in the table that allows them to be joined or merged While these are the hard and fast rules, there are a number of other things that will make your data set much easier to handle. First is to include a row at the top of each data table/spreadsheet that contains full row names. So if you measured age at diagnosis for patients, you would head that column with the name AgeAtDiagnosis instead of something like ADx or another abbreviation that may be hard for another person to understand. Here is an example of how this would work from genomics. Suppose that for 20 people you have collected gene expression measurements with RNA-sequencing. You have also collected demographic and clinical information about the patients including their age, treatment, and diagnosis. You would have one table/spreadsheet that contains the clinical/demographic information. It would have four columns (patient id, age, treatment, diagnosis) and 21 rows (a row with variable names, then one row for every patient). You would also have one spreadsheet for the summarized genomic data. Usually this type of data is summarized at the level of the number of counts per exon. Suppose you have 100,000 exons, then you would have a table/spreadsheet that had 21 rows (a row for gene names, and one row for each patient) and 100,001 columns (one row for patient ids and one row for each data type). If you are sharing your data with the collaborator in Excel, the tidy data should be in one Excel file per table. They should not have multiple worksheets, no macros should be applied to the data, and no columns/cells should be highlighted. Alternatively share the data in a CSV or TAB-delimited text file. (Beware however that reading CSV files into Excel can sometimes lead to non-reproducible handling of date and time variables.) The code book For almost any data set, the measurements you calculate will need to be described in more detail than you can or should sneak into the spreadsheet. The code book contains this information. At minimum it should contain: Information about the variables (including units!) in the data set not contained in the tidy data Information about the summary choices you made Information about the experimental study design you used In our genomics example, the analyst would want to know what the unit of measurement for each clinical/demographic variable is (age in years, treatment by name/dose, level of diagnosis and how heterogeneous). They would also want to know how you picked the exons you used for summarizing the genomic data (UCSC/Ensembl, etc.). They would also want to know any other information about how you did the data collection/study design. For example, are these the first 20 patients that walked into the clinic? Are they 20 highly selected patients by some characteristic like age? Are they randomized to treatments? A common format for this document is a Word file. There should be a section called "Study design" that has a thorough description of how you collected the data. There is a section called "Code book" that describes each variable and its units. How to code variables When you put variables into a spreadsheet there are several main categories you will run into depending on their data type: Continuous Ordinal Categorical Missing Censored Continuous variables are anything measured on a quantitative scale that could be any fractional number. An example would be something like weight measured in kg. Ordinal data are data that have a fixed, small (< 100) number of levels but are ordered. This could be for example survey responses where the choices are: poor, fair, good. Categorical data are data where there are multiple categories, but they aren't ordered. One example would be sex: male or female. This coding is attractive because it is self-documenting. Missing data are data that are unobserved and you don't know the mechanism. You should code missing values as NA. Censored data are data where you know the missingness mechanism on some level. Common examples are a measurement being below a detection limit or a patient being lost to follow-up. They should also be coded as NA when you don't have the data. But you should also add a new column to your tidy data called, "VariableNameCensored" which should have values of TRUE if censored and FALSE if not. In the code book you should explain why those values are missing. It is absolutely critical to report to the analyst if there is a reason you know about that some of the data are missing. You should also not impute/make up/ throw away missing observations. In general, try to avoid coding categorical or ordinal variables as numbers. When you enter the value for sex in the tidy data, it should be "male" or "female". The ordinal values in the data set should be "poor", "fair", and "good" not 1, 2 ,3. This will avoid potential mixups about which direction effects go and will help identify coding errors. Always encode every piece of information about your observations using text. For example, if you are storing data in Excel and use a form of colored text or cell background formatting to indicate information about an observation ("red variable entries were observed in experiment 1.") then this information will not be exported (and will be lost!) when the data is exported as raw text. Every piece of data should be encoded as actual text that can be exported. The instruction list/script You may have heard this before, but reproducibility is a big deal in computational science. That means, when you submit your paper, the reviewers and the rest of the world should be able to exactly replicate the analyses from raw data all the way to final results. If you are trying to be efficient, you will likely perform some summarization/data analysis steps before the data can be considered tidy. The ideal thing for you to do when performing summarization is to create a computer script (in R, Python, or something else) that takes the raw data as input and produces the tidy data you are sharing as output. You can try running your script a couple of times and see if the code produces the same output. In many cases, the person who collected the data has incentive to make it tidy for a statistician to speed the process of collaboration. They may not know how to code in a scripting language. In that case, what you should provide the statistician is something called pseudocode. It should look something like: Step 1 - take the raw file, run version 3.1.2 of summarize software with parameters a=1, b=2, c=3 Step 2 - run the software separately for each sample Step 3 - take column three of outputfile.out for each sample and that is the corresponding row in the output data set You should also include information about which system (Mac/Windows/Linux) you used the software on and whether you tried it more than once to confirm it gave the same results. Ideally, you will run this by a fellow student/labmate to confirm that they can obtain the same output file you did. What you should expect from the analyst When you turn over a properly tidied data set it dramatically decreases the workload on the statistician. So hopefully they will get back to you much sooner. But most careful statisticians will check your recipe, ask questions about steps you performed, and try to confirm that they can obtain the same tidy data that you did with, at minimum, spot checks. You should then expect from the statistician: An analysis script that performs each of the analyses (not just instructions) The exact computer code they used to run the analysis All output files/figures they generated. This is the information you will use in the supplement to establish reproducibility and precision of your results. Each of the steps in the analysis should be clearly explained and you should ask questions when you don't understand what the analyst did. It is the responsibility of both the statistician and the scientist to understand the statistical analysis. You may not be able to perform the exact analyses without the statistician's code, but you should be able to explain why the statistician performed each step to a labmate/your principal investigator. Contributors Jeff Leek - Wrote the initial version. L. Collado-Torres - Fixed typos, added links. Nick Reich - Added tips on storing data as text. Nick Horton - Minor wording suggestions.

Udacity Data Analyst Nanodegree Project - Wrangle and Analyze WeRateDogs Twitter account.

A simple twitter data analyst. That create graphs based on twitter data.

Voici le début d'un programme d'analyse sentimental des tweets

No description available

No description available

Twitter Analyst App based on NodeJS and LokiJS

Python Twitter Simple Sentiement Analyst With Tweepy

No description available

Udacity Data Analyst Nanodegree Project 4 - WeRateDogs Twitter Data Wrangling & Analysis