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Social Analyzer includes sophisticated string analysis capabilities to decompose usernames into their constituent parts, identify patterns, and generate variations for comprehensive profile searches.

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Overview

The string analysis module (string-analysis.js) performs multiple operations:
  • Name extraction and identification
  • Prefix and suffix detection
  • Number and symbol extraction
  • Word permutations and combinations
  • Common word identification by language
  • Age estimation from patterns
  • Leetspeak conversion

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Name Analysis

The analyzer uses dictionaries of male and female names to identify name components within usernames.

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Name Extraction

From string-analysis.js:91-108, the analyzer searches for known names: 
// Search for male names
helper.parsed_json.m_names.forEach(function (item, index) {
  if (string_to_check.indexOf(item) >= 0 && !all_words.name.includes(item)) {
    all_words.name.push(item)
    const temp = remove_word(string_to_check, item)
    if (temp !== null && temp !== '' && temp.length > 1) {
      all_words.unknown.push(temp)
    }
  }
})

// Search for female names
helper.parsed_json.f_names.forEach(function (item, index) {
  if (string_to_check.indexOf(item) >= 0 && !all_words.name.includes(item)) {
    all_words.name.push(item)
    const temp = remove_word(string_to_check, item)
    if (temp !== null && temp !== '' && temp.length > 1) {
      all_words.unknown.push(temp)
    }
  }
})

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Example

// Input: "johndoe123"
// Detected:
// - name: ["john"]
// - unknown: ["doe123", "doe"]
// - number: ["123"]

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Prefix Detection

Identifies common username prefixes like “the”, “real”, “official”, etc. From string-analysis.js:82-90: 
helper.parsed_json.prefix.forEach(function (item, index) {
  if (string_to_check.indexOf(item) === 0 && !all_words.prefix.includes(item)) {
    all_words.prefix.push(item)
    const temp = remove_word(string_to_check, item)
    if (temp !== null && temp !== '' && temp.length > 1) {
      all_words.unknown.push(temp)
    }
  }
})
Prefix detection only matches at the start of the string (position 0) to avoid false positives.

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Symbol and Number Extraction

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Symbol Detection

From string-analysis.js:172-180, extracts special characters: 
async function find_symbols (req, all_words) {
  try {
    req.body.string.match(/[ \[\]:"\\|,.<>\/?~`!@#$%^&*()_+\-={};']/gi)
      .forEach((item) => {
        if (item !== ' ' && !all_words.symbol.includes(item)) {
          all_words.symbol.push(item)
        }
      })
  } catch (err) {}
}

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Number Extraction

From string-analysis.js:182-190, identifies numeric patterns: 
async function find_numbers (req, all_words) {
  try {
    req.body.string.match(/(\d+)/g).forEach((item) => {
      if (!all_words.number.includes(item)) {
        all_words.number.push(item)
      }
    })
  } catch (err) {}
}

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Example

// Input: "john_doe@2024!"
// Detected:
// - symbol: ["_", "@", "!"]
// - number: ["2024"]

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String Splitting Techniques

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Split by Comma

From string-analysis.js:142-150, handles comma-separated searches: 
async function split_comma (req, all_words) {
  try {
    req.body.string.split(',').forEach((item) => {
      if (item.length > 1 && !all_words.unknown.includes(item)) {
        all_words.unknown.push(item.toLowerCase())
      }
    })
  } catch (err) {}
}

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Split by Uppercase

From string-analysis.js:152-160, detects camelCase patterns: 
async function split_upper_case (req, all_words) {
  try {
    req.body.string.match(/[A-Z][a-z]+/g).forEach((item) => {
      if (item.length > 1 && !all_words.unknown.includes(item)) {
        all_words.unknown.push(item.toLowerCase())
      }
    })
  } catch (err) {}
}

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Split by Alphabet

From string-analysis.js:162-170, extracts all alphabetic sequences: 
async function split_alphabet_case (req, all_words) {
  try {
    req.body.string.match(/[A-Za-z]+/g).forEach((item) => {
      if (item.length > 1 && !all_words.unknown.includes(item)) {
        all_words.unknown.push(item.toLowerCase())
      }
    })
  } catch (err) {}
}

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Leetspeak Conversion

Converts common number-to-letter substitutions used in usernames. From string-analysis.js:192-216: 
async function convert_numbers (req) {
  const numbers_to_letters = {
    4: 'a',
    8: 'b',
    3: 'e',
    1: 'l',
    0: 'o',
    5: 's',
    7: 't',
    2: 'z'
  }

  let temp_value = ''
  for (let i = 0; i < req.body.string.length; i++) {
    const _temp = numbers_to_letters[req.body.string.charAt(i)]
    if (_temp !== undefined) {
      temp_value += numbers_to_letters[req.body.string.charAt(i)]
    } else {
      temp_value += req.body.string.charAt(i)
    }
  }
  req.body.string = temp_value
}

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Example

// Input: "h4ck3r123"
// Converted: "hacker123"
// Mapping:
// - 4 -> a
// - 3 -> e
Leetspeak conversion happens before other analysis steps to maximize name detection accuracy.

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Word Permutations

The analyzer uses WordsNinja to split concatenated words and identify meaningful components. From string-analysis.js:218-223: 
async function get_maybe_words (req, all_words) {
  await WordsNinja.loadDictionary()
  all_words.maybe = await WordsNinja.splitSentence(req.body.string)
    .filter(function (elem, index, self) {
      return index === self.indexOf(elem)
    })
    .filter(word => word.length > 1)
}

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Example

// Input: "johnsmith"
// WordsNinja splits to: ["john", "smith"]

// Input: "therockingjohn"
// WordsNinja splits to: ["the", "rocking", "john"]

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Language Detection

Identifies the language origin of words found in usernames. From string-analysis.js:30-52: 
async function most_common (all_words, temp_words) {
  const temp_list = []
  Object.keys(all_words).forEach(function (key) {
    all_words[key].forEach(function (item) {
      if (!temp_list.includes(item) && item.length > 1) {
        temp_list.push(item)
        const temp = findWord(item)
        if (Object.keys(temp).length !== 0) {
          const languages = Object.keys(temp).map(function (key) {
            return [key, temp[key]]
          })
          languages.sort(function (first, second) {
            return second[1] - first[1]
          }).reverse()
          temp_words.push({
            word: item,
            languages: languages.map(e => e[0]).join(', ')
          })
        }
      }
    })
  })
}

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Example Output

[
  {
    "word": "john",
    "languages": "en, de, nl"
  },
  {
    "word": "pierre",
    "languages": "fr, en"
  }
]

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Age Estimation

Attempts to estimate age from numeric patterns in usernames. From string-analysis.js:225-270: 
async function guess_age_from_string(req) {
  let results = []
  let age_4_numbers = /\d{4}|\d{2}/g
  let current_year = new Date().getFullYear()
  
  while ((match = age_4_numbers.exec(req.body.string)) != null) {
    let temp_dict = {"found":"","year":"","age":""}
    let found = parseInt(match[0])
    
    // Check if 2-digit year between 50-99 (1950-1999)
    if (found >= 50 && found <= 99){
      year = found + 1900
      age = current_year - year
      if (age <= 75){
        temp_dict.year = year.toString()
        temp_dict.age = age.toString()
      }
    }
    
    // Check if direct age between 14-49
    if (found >= 14 && found <= 49){
      year = current_year - found
      age = found
      if (age <= 75){
        temp_dict.year = year.toString()
        temp_dict.age = age.toString()
      }
    }
    
    // Check if 4-digit birth year
    if (found >= 1950){
      year = found
      age = current_year - year
      if (age <= 75){
        temp_dict.year = year.toString()
        temp_dict.age = age.toString()
      }
    }
    
    results.push(temp_dict)
  }
  
  return results
}

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Example

// Input: "johndoe1985"
// Age estimation:
// {
//   "found": "1985",
//   "year": "1985",
//   "age": "39"
// }

// Input: "user_25_smith"
// Age estimation:
// {
//   "found": "25",
//   "year": "1999",
//   "age": "25"
// }
Age estimation is limited and should be used as a hint rather than definitive information. Many usernames contain numbers unrelated to age.

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Analysis Workflow

The complete string analysis follows this sequence:
  1. Convert Numbers: Apply leetspeak conversion
  2. Find Symbols: Extract special characters
  3. Find Numbers: Extract numeric sequences
  4. Split Patterns: Apply comma, uppercase, and alphabet splitting
  5. Analyze String: Match against name and prefix dictionaries
  6. Word Ninja: Split concatenated words
  7. Language Detection: Identify word origins
  8. Age Estimation: Extract possible age information

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Multi-Profile Analysis

Social Analyzer supports comma-separated usernames for correlation analysis: 
node app.js --username "johndoe,janedoe,jdoe" --metadata
This analyzes multiple related profiles simultaneously and can identify patterns across accounts.

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Practical Examples

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Example 1: Complex Username

// Input: "TheRealJohn123_Official"

// Analysis results:
// prefix: ["the", "real", "official"]
// name: ["john"]
// number: ["123"]
// symbol: ["_"]
// unknown: ["john123", "john"]

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Example 2: Leetspeak Username

// Input: "h4x0r_j0hn"

// After conversion: "haxor_john"
// name: ["john"]
// maybe: ["haxor", "john"]
// symbol: ["_"]

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Example 3: Multi-word Username

// Input: "johnsmithcoder"

// Analysis results:
// name: ["john"]
// maybe: ["john", "smith", "coder"]
// languages: 
//   - john: en, de
//   - smith: en
//   - coder: en

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Integration with Detection

String analysis results feed into the profile detection process by:
  • Generating username variations to search
  • Identifying related profiles through name matching
  • Providing context for detected profiles
  • Enabling correlation across multiple usernames
The decomposed strings are used to build comprehensive search patterns that increase detection accuracy across different platforms.

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