PROFANE WORDS TRACKING, DETECTION, AND PREVENTION SYSTEM

OUR PROJECT IS FUNDED BY KOSGEB R&D, INNOVATION AND INDUSTRIAL APPLICATIONS SUPPORT PROGRAMME OF VERDICT NO. 2016-701-36.

1

PROFANITY ON WEB AND SOCIAL MEDIA İnternet ve Sosyal Medyada Hakaret ve Küfür

2

PROFANE WORDS § There is no need for any diplomas, knowledge and proficiency,

intelligence in order to be insulted or swearing. § You can also curse by writing, § Profanity and insult are expressions of inability, § The mood most suitable for swearing is V. § profanity is just an empty threat; the insult is to attribute a unilateral baseless nature, § profanity and insults abolish the persecutors of the violent will and offense; as a person satisfies his or her own soul at a pathological / disease level, § Defamation / profanity target and honor human dignity and honor, § It targets and offends the image and brand as a corporate, § Prevention of insult and cursing protects the dignity and honor of the person, the reputation, the image and the brand of the institution!

3

SOFTWARE OUT OF NEED § The Internet is an indispensable source of information and a perfect tool in

communicating as well as a technological tool that produces many harmful effects,

§ Detection and prevention of internet-borne factors that leave negative traces on

human beings gain importance,

§ People can be protected from internet damages by providing legal conditions by the

state, and they can be protected in a smart way with the help of technology.

§ Using technology (eg internet filtering programs); It can be used as the primary

prevention method and the most effective way of fighting, which aims at preventing the internet problem at the point of exit and preventing it from occurring.

§ Thus, it is possible to reduce the damage, insecurity, violence and crime by attempting

to eliminate the scientifically determined causal factors (risk factors) successfully,

§ Thus, labor, time and money, benefits, etc., will be spent to prevent damage and

protect against crime; there will always be little after the damage occurred. Costeffectiveness can be achieved through the use of technological smart systems on human resources and social media to prevent damage and harm.

4

REDUCTION: HUMAN ERRORS ON MODERATION § Not to be familiar with the specific concepts § To translate

to idioms,

of the text,

one to one without looking at the dictionary when it comes

§ To distinguish the discourse record of the text and not

correctly, § Not reading the text carefully, § Turkish word-line

to transfer it

error + not understanding the meaning of the text,

§ Turkish grammar weakness

§ NOONE IS PERFECT: § 99% of people leave the error behind (An American Copyeditor), § A trained professional works at a 95% accuracy rate, leaving a 5% error

(Editors, Scholars, and the Social Text, Dr. Panko of the University of Hawaii) § 80% accuracy rate is expected from people (Editor’s Association of Canada)

5

REDUCTION: HUMAN ERRORS ON MODERATION

6

THE CONCEPT AND CONTEXT OF THE SOFTWARE § In the current market; Initiatives for defamatory determination and detection purposes and

software application are traditionally formed by creating a certain profanity dictionary and looking at whether one word is contained in the searched content as one-to-one exact match,

§ No smart debugging, learning new or uncommitted words can be done by other traditional

systems

§ This filtering is done by certain admins / editors; if legal process is requested, it is started

with human power,

§ Instead of doing the main job of the admin / redirector has to clear many insulting / abusive

texts. This situation leads to loss of morale and time.

§ Brand and image owners face a lot of insults / profanity on the internet, which damages

dignity and dignity,

§ As a second field, products that make content-based social media analysis have a

categorization of content by means of categorization method; In a technological level, insult / profanity cannot be followed, and only after the determination and profanity are formed, the person is followed up, or a defamation determined by manual manpower, for example, tries to determine the source of profanity going back from the RTs in twitter.

§ Therefore; § A machine learning-based learning system with one-on-one profanity equivalent words (add,

subtract, change, etc.) can also catch the derived profanity and insults, and the owner of the personal information related to the profanity of the process to obtain at a fully automated way; § To be able to prevent pre-filtering of profanity without human need and also to be able to analyze and identify the necessary curses and manage the necessary legal process; § There is a need for a system.

7

BUZZ-PREVENTER § Is

a system that works on Internet environment; § which can detect profanity / insult, § which can prevent the insult source,

§ preventing § Able to

the transmission and transmission of

self-learn and eliminate human effort,

§ Machine Learning

(ML) based,

§ Increases

the accuracy rate by providing adaptation to new data over time,

§ Preventing

insulting / cursing

8

AREA OF USE

9

WORKING PRINCIPLE Finds root, index of the profane word and relates to an original one if derived Original Text

Makes analysis on the text and derives data

10

BUZZ-ANALYSER § Is

a system working on Internet environment; § which can detect profanity / insult, § which can identify the insult / profanity source, § which can identify the originator and the source,

§ Self-learning

and eliminating human effort,

§ Machine Learning

(ML) based,

§ Increases

the accuracy rate by providing adaptation to new data over time,

§ Identify § It can

insults / profanities

be reported.

11

AREA OF USE

12

WORKING PRINCIPLE Streaming of data source and making analysis

Pre and post analysis using Machine Learning and statistical approach

13

EXAMPLE REPORT Daily Analysis

Linking to data source containing profanity

Results

14

EXAMPLE REPORT Daily topic analysis for profanity on the data sources: deriving most used words and topics

15

EXAMPLE DEMO

Trie Parsing, NLP, Text distance, Preprocessing and Machine Learning analysis and results on text

16

DEMO RESULT’S MEANING § 0 = No profanity § -1 determined directly from the dictionary of insult / profanity

detection (about 1,000 Turkish words and phrase / proverb-sentence based detection), 1,000 English insults and profanity,)

§ -2 misspelled words, suspicious word detection, similar word

capture

§ -3 Dependency Parsing -> untyped cascaded chunking

17

CUSTOMER FEEDBACK FORMS ยง The

system can detect and prevent insult / cursing at a rate of 80% and goes up to 90% including preprocessing stage.

ยงA

feedback form has been developed for improving Machine Learning trainings and developing language features,

ยง With

this form, our customers can tell us the possible Texts that contain the insult / profanity that Buzz-Preventer cannot detect,

ยง These texts

are included in ML model by examining,

ยง The

model is included in ML model by performing necessary tests once a month and a new model is represented that learned from its mistakes.

18

INNOVATIVE SIDES § SPEED:

No matter how large the text is, it can be reached very quickly thanks to the special algorithms we use (Asynchronous servlet queue is used): § § § § §

300 words: 250ms - rps: 10 500 words: 341ms - rps: 7 1000 words: 547ms - rps: 4 1500 words: 798ms - rps: 2 3000 words: 1.58s - rps: 0.8

§ NATURAL LANGUAGE PROCESSING (NLP):

By finding the roots of the words with the help of NLP, it can detect any insult / profanity, no matter what additional supplements it takes. In addition, the suspicious word name / adjective / pronoun, such as finding features by providing additional input to Machine Learning (ML) to help decide.

§ CLEARING SPECIAL CHARACTERS:

All non-letter special characters in text are automatically cleaned and processed after language processing. In this way, the user can determine what kind of character he / she wrote into the suspicious word and the insult / profanity.

§ EASY INTEGRATION:

The product is put into service by setting up a virtual server in a short time and by opening the service. You can get results from the feedback form, customer comments or chat environments using the relevant service. The result is returned with None / None and the index is returned with the start ending value.

§ DETECTING SIMILAR OR DERIVED PROFANE WORDS:

capture any

When our product does not

19

INNOVATIVE SIDES ยง CREATING SPECIFIC DB for every Customer:

to your company's insult / profanity DB.

This feature allows you to easily add data

ยง WHITE LIST:

Even if the insult / profanity is in the DB or as a result of ML, it may allow the entry of the system into the related words. For example, in the form of feedback, allowing the writing of the relevant suspect word can only be made in the form of product specifications.

ยง MACHINE LEARNING (ML):

If no suspicious words have been captured in the relevant text or the suspicious word has been taken in any way and evolved, the text whole text NING is sent to ML. ML evaluates all text and makes a decision. Thus, basic insult / blasphemous DB (about 1,000 words root) words can be determined whether the kรถ insult / profanity (contains. ML does not make one-to-one comparisons and evaluates the text as a whole. The result is returned according to the decision made by SVM algorithm as a result of vector analysis.

ยง SELF-LEARNING:

The ML part of our product is 80% accuracy and our system works with a accuracy of over 90% with pre-sorting result. In spite of this, these texts are integrated with a service in our product by a button that you add to your moderator / customer representative in your system, so these texts come back to us as sistem feedback size. can be easily taught to our product.

20

INNOVATIVE SIDES § CLEANING SPECIAL CHARACTERS: The

product cleans all special characters in the tet content. For example, when the word bilmek shit ”is written as” b2o.k 2, the product knows this word as imes shit Örneğin.

§ SEPARATION OF THE WORDS: The

product finds the root of the word with NLP root analysis. For example, the word assbabe has two words and appears as a compound one, or it might have been a derived word as well.

§ CONTEXT BASED DETECTION:

profanity.

Depending on the context, the system tries to identify

§ SIMILARITY ANALYSIS:

Our product is like insult / profanity and can detect suspicious words by similarity analysis. For example, even though the word is written differently, it can be detenced since it does not have a dictionary type memory. The words are to be examined by more than one step and approach, also including suspected word detection stage which labels a text as suspiciously profane. These steps might be modified depending on the sensitivity level required.

21

BUZZ-PREVENTER LICENSE USAGE § ICT centers

of our customers «at least» «1» virtual server is based on performance criteria.

§ After

the installation, the web-service is opened and the content of the Texts coming from the system is checked to see whether there is any insult / profanity content and the information is returned to the related system.

§ With

the feedback form found in our company, Texts that can be found in 20% are collected and monthly ML model update is made.

§ The ML model is

virtual server.

updated every month on our customers'

22

BUZZ-ANALYSER LICENSE USAGE ยง Determining

social media and / or related websites to be analyzed with our customers,

ยง Keywords

are determined,

ยง Related

social media and / or internet site daily / weekly / monthly screening,

ยง The relevant insult / abusive resources

report is produced and delivered.

are identified and the

ยง With

the feedback form found in our company, Texts that can be found in 20% are collected and monthly ML model update is made.

23

PATENT APPLICATION FOR OUR SOFTWARE

24

Protect your Brand and Image!

For Detailed Information: Dr. Recep TÜRKOĞLU info@rsh.ee www.rsh.ee Tel : +90 (555) 474 45 55 25