Social Intelligence Agent
Social Media Integration
Component Overview
The Social Intelligence Agent serves as Kaizen AI's comprehensive social media monitoring and analysis system, responsible for collecting, processing, and analyzing social signals across multiple platforms to assess community health, detect manipulation, and evaluate genuine sentiment around cryptocurrency projects.
Core Responsibilities:
Multi-platform social media data collection and aggregation
Advanced natural language processing for sentiment and context analysis
Real-time manipulation detection and coordinated behavior identification
Community health assessment and engagement authenticity verification
Influencer impact analysis and network mapping
Viral content analysis and organic growth validation
Architecture Philosophy: The Social Intelligence Agent employs a distributed, fault-tolerant architecture that can process thousands of social media posts per minute while maintaining accuracy in sentiment analysis and manipulation detection. The system prioritizes real-time processing, scalable data collection, and sophisticated linguistic analysis to provide actionable insights for risk assessment.
Multi-Platform Data Collection
Platform Integration Architecture
// Core interfaces for multi-platform social media integration
interface SocialPlatform {
readonly name: string;
readonly apiVersion: string;
readonly rateLimits: RateLimitConfig;
readonly authConfig: AuthenticationConfig;
readonly capabilities: PlatformCapability[];
}
interface SocialMediaCollector {
platform: SocialPlatform;
isConnected: boolean;
lastSync: Date;
connect(): Promise<void>;
disconnect(): Promise<void>;
collectMentions(query: SocialQuery): Promise<SocialPost[]>;
streamRealTime(filters: StreamFilter[]): AsyncIterator<SocialPost>;
getUserInfo(userId: string): Promise<UserProfile>;
validateConnection(): Promise<boolean>;
}
export class SocialIntelligenceOrchestrator {
private collectors: Map<string, SocialMediaCollector> = new Map();
private dataProcessor: SocialDataProcessor;
private sentimentAnalyzer: SentimentAnalyzer;
private manipulationDetector: ManipulationDetector;
private communityAnalyzer: CommunityAnalyzer;
private realTimeStreams: Map<string, StreamManager> = new Map();
constructor(
private config: SocialIntelligenceConfig,
private database: DatabaseInterface,
private messageQueue: MessageQueueInterface,
private cache: CacheInterface
) {
this.dataProcessor = new SocialDataProcessor(config.processing);
this.sentimentAnalyzer = new SentimentAnalyzer(config.sentiment);
this.manipulationDetector = new ManipulationDetector(config.manipulation);
this.communityAnalyzer = new CommunityAnalyzer(config.community);
this.initializePlatformCollectors();
this.setupRealTimeStreaming();
}
private async initializePlatformCollectors() {
const platforms = [
new TwitterCollector(this.config.twitter),
new TelegramCollector(this.config.telegram),
new DiscordCollector(this.config.discord),
new FarcasterCollector(this.config.farcaster),
new RedditCollector(this.config.reddit)
];
for (const collector of platforms) {
try {
await collector.connect();
this.collectors.set(collector.platform.name, collector);
logger.info(`Connected to ${collector.platform.name} API`);
// Set up error handling and reconnection
this.setupCollectorMonitoring(collector);
} catch (error) {
logger.error(`Failed to connect to ${collector.platform.name}:`, error);
// Schedule retry with exponential backoff
this.scheduleRetryConnection(collector, 1);
}
}
}
async analyzeSocialSignals(projectQuery: ProjectSocialQuery): Promise<SocialIntelligenceResult> {
const startTime = Date.now();
try {
// Collect data from all platforms in parallel
const collectionPromises = Array.from(this.collectors.values()).map(async collector => {
try {
const posts = await collector.collectMentions({
tokens: projectQuery.tokenSymbols,
contractAddresses: projectQuery.contractAddresses,
timeframe: projectQuery.timeframe,
includeReplies: true,
includeRetweets: false
});
return { platform: collector.platform.name, posts, error: null };
} catch (error) {
logger.warn(`Data collection failed for ${collector.platform.name}:`, error);
return { platform: collector.platform.name, posts: [], error: error.message };
}
});
const collectionResults = await Promise.all(collectionPromises);
// Aggregate all collected posts
const allPosts = collectionResults.flatMap(result => result.posts);
if (allPosts.length === 0) {
return this.generateEmptyResult(projectQuery, 'No social media mentions found');
}
// Process collected data through analysis pipeline
const processedData = await this.dataProcessor.processRawPosts(allPosts);
// Perform sentiment analysis
const sentimentResult = await this.sentimentAnalyzer.analyzeBatch(processedData);
// Detect manipulation and coordinated behavior
const manipulationResult = await this.manipulationDetector.analyzeManipulation(processedData);
// Assess community health
const communityResult = await this.communityAnalyzer.assessCommunityHealth(processedData);
// Calculate overall social intelligence score
const socialScore = this.calculateSocialScore(
sentimentResult,
manipulationResult,
communityResult
);
const processingTime = Date.now() - startTime;
return {
projectQuery,
socialScore,
sentiment: sentimentResult,
manipulation: manipulationResult,
community: communityResult,
platforms: this.generatePlatformSummary(collectionResults),
metadata: {
totalPosts: allPosts.length,
processingTime,
dataQuality: this.assessDataQuality(processedData),
analysisTimestamp: new Date().toISOString()
}
};
} catch (error) {
logger.error('Social intelligence analysis failed:', error);
throw new SocialIntelligenceError('Social analysis failed', error);
}
}
}Twitter Integration
Advanced Twitter Data Collection
// Twitter API v2 integration with enhanced features
export class TwitterCollector implements SocialMediaCollector {
public readonly platform: SocialPlatform;
private client: TwitterApi;
private rateLimiter: RateLimiter;
private streamConnection: TwitterStream | null = null;
private userCache: Map<string, TwitterUser> = new Map();
constructor(private config: TwitterConfig) {
this.platform = {
name: 'twitter',
apiVersion: 'v2',
rateLimits: {
searchTweets: { requests: 300, window: 900000 }, // 15 minutes
userLookup: { requests: 300, window: 900000 },
streaming: { connections: 1, tweets: 1000000 } // per month
},
authConfig: config.auth,
capabilities: ['mentions', 'sentiment', 'engagement', 'user_metrics']
};
this.client = new TwitterApi({
appKey: config.auth.apiKey,
appSecret: config.auth.apiSecret,
accessToken: config.auth.accessToken,
accessSecret: config.auth.accessTokenSecret,
bearerToken: config.auth.bearerToken
});
this.rateLimiter = new RateLimiter(this.platform.rateLimits);
}
async connect(): Promise<void> {
try {
// Verify credentials
const me = await this.client.currentUser();
logger.info(`Connected to Twitter as @${me.username}`);
this.isConnected = true;
this.lastSync = new Date();
} catch (error) {
logger.error('Twitter connection failed:', error);
throw new ConnectionError('Twitter API connection failed', error);
}
}
async collectMentions(query: SocialQuery): Promise<SocialPost[]> {
try {
// Build Twitter search query
const searchQuery = this.buildTwitterQuery(query);
// Rate limit check
await this.rateLimiter.waitForAvailability('searchTweets');
// Search tweets with comprehensive fields
const searchResults = await this.client.v2.search(searchQuery, {
'tweet.fields': [
'created_at',
'author_id',
'public_metrics',
'context_annotations',
'conversation_id',
'in_reply_to_user_id',
'referenced_tweets',
'lang',
'possibly_sensitive',
'reply_settings'
],
'user.fields': [
'created_at',
'public_metrics',
'verified',
'description',
'location',
'profile_image_url'
],
'expansions': [
'author_id',
'referenced_tweets.id',
'referenced_tweets.id.author_id'
],
max_results: Math.min(query.limit || 100, 100),
start_time: query.timeframe.start.toISOString(),
end_time: query.timeframe.end.toISOString()
});
const posts: SocialPost[] = [];
// Process each tweet
for (const tweet of searchResults.data || []) {
const author = searchResults.includes?.users?.find(u => u.id === tweet.author_id);
if (!author) continue;
const processedPost = await this.processTwitterPost(tweet, author, searchResults.includes);
posts.push(processedPost);
}
return posts;
} catch (error) {
logger.error('Twitter mention collection failed:', error);
throw new DataCollectionError('Twitter data collection failed', error);
}
}
private buildTwitterQuery(query: SocialQuery): string {
const queryParts: string[] = [];
// Add token symbols
if (query.tokens && query.tokens.length > 0) {
const tokenQueries = query.tokens.map(token => `$${token} OR #${token}`);
queryParts.push(`(${tokenQueries.join(' OR ')})`);
}
// Add contract addresses
if (query.contractAddresses && query.contractAddresses.length > 0) {
const addressQueries = query.contractAddresses.map(addr => `"${addr}"`);
queryParts.push(`(${addressQueries.join(' OR ')})`);
}
// Language filter
queryParts.push('lang:en');
// Exclude retweets if specified
if (!query.includeRetweets) {
queryParts.push('-is:retweet');
}
// Exclude replies if specified
if (!query.includeReplies) {
queryParts.push('-is:reply');
}
return queryParts.join(' ');
}
private async processTwitterPost(
tweet: TweetV2,
author: UserV2,
includes?: Includes2
): Promise<SocialPost> {
// Calculate user authenticity score
const authorAuthenticity = this.calculateUserAuthenticity(author);
// Extract entities and context
const entities = this.extractTwitterEntities(tweet);
// Calculate engagement metrics
const engagementScore = this.calculateEngagementScore(tweet.public_metrics!);
return {
id: tweet.id,
platform: 'twitter',
content: tweet.text,
author: {
id: author.id,
username: author.username,
displayName: author.name,
verified: author.verified || false,
followerCount: author.public_metrics?.followers_count || 0,
followingCount: author.public_metrics?.following_count || 0,
createdAt: author.created_at,
profileDescription: author.description,
authenticity: authorAuthenticity
},
timestamp: new Date(tweet.created_at!),
metrics: {
likes: tweet.public_metrics?.like_count || 0,
shares: tweet.public_metrics?.retweet_count || 0,
replies: tweet.public_metrics?.reply_count || 0,
quotes: tweet.public_metrics?.quote_count || 0,
engagement: engagementScore
},
entities,
metadata: {
conversationId: tweet.conversation_id,
language: tweet.lang,
possiblySensitive: tweet.possibly_sensitive,
contextAnnotations: tweet.context_annotations
}
};
}
private calculateUserAuthenticity(user: UserV2): number {
let score = 0.5; // Base score
// Account age factor (older accounts more trustworthy)
if (user.created_at) {
const accountAge = Date.now() - new Date(user.created_at).getTime();
const ageInDays = accountAge / (1000 * 60 * 60 * 24);
if (ageInDays > 365) score += 0.2; // 1+ years
else if (ageInDays > 180) score += 0.1; // 6+ months
else if (ageInDays < 30) score -= 0.2; // Less than 30 days
}
// Follower ratio (balanced following/follower ratio)
if (user.public_metrics) {
const followers = user.public_metrics.followers_count || 0;
const following = user.public_metrics.following_count || 1; // Avoid division by zero
const ratio = followers / following;
if (ratio > 0.1 && ratio < 10) score += 0.1; // Balanced ratio
else if (ratio < 0.01 || ratio > 100) score -= 0.2; // Suspicious ratio
}
// Verification status
if (user.verified) score += 0.3;
// Profile completeness
if (user.description && user.description.length > 20) score += 0.1;
if (user.location) score += 0.05;
if (user.profile_image_url && !user.profile_image_url.includes('default')) score += 0.05;
return Math.max(0, Math.min(1, score));
}
async streamRealTime(filters: StreamFilter[]): Promise<AsyncIterator<SocialPost>> {
try {
// Build streaming rules
const rules = filters.map(filter => ({
value: this.buildTwitterQuery(filter.query),
tag: filter.tag || `filter_${Date.now()}`
}));
// Set up streaming rules
await this.client.v2.updateStreamRules({
add: rules
});
// Create stream
this.streamConnection = await this.client.v2.searchStream({
'tweet.fields': [
'created_at',
'author_id',
'public_metrics',
'context_annotations',
'lang'
],
'user.fields': [
'public_metrics',
'verified',
'created_at'
],
expansions: ['author_id']
});
return this.createAsyncIterator();
} catch (error) {
logger.error('Twitter streaming setup failed:', error);
throw new StreamingError('Twitter streaming failed', error);
}
}
private async *createAsyncIterator(): AsyncIterator<SocialPost> {
if (!this.streamConnection) {
throw new Error('Stream connection not established');
}
try {
for await (const { data: tweet, includes } of this.streamConnection) {
if (tweet && includes?.users) {
const author = includes.users.find(u => u.id === tweet.author_id);
if (author) {
const processedPost = await this.processTwitterPost(tweet, author, includes);
yield processedPost;
}
}
}
} catch (error) {
logger.error('Twitter stream error:', error);
throw error;
}
}
}Telegram Integration
Telegram Bot and Channel Monitoring
// Telegram data collection through bot API and channel monitoring
export class TelegramCollector implements SocialMediaCollector {
public readonly platform: SocialPlatform;
private bot: TelegramBot;
private channelMonitor: ChannelMonitor;
private messageProcessor: TelegramMessageProcessor;
constructor(private config: TelegramConfig) {
this.platform = {
name: 'telegram',
apiVersion: 'bot_api_6.0',
rateLimits: {
messages: { requests: 30, window: 1000 }, // 30 per second
channelInfo: { requests: 20, window: 60000 } // 20 per minute
},
authConfig: config.auth,
capabilities: ['channels', 'groups', 'mentions', 'sentiment']
};
this.bot = new TelegramBot(config.auth.botToken, { polling: false });
this.channelMonitor = new ChannelMonitor(config.channels);
this.messageProcessor = new TelegramMessageProcessor();
}
async connect(): Promise<void> {
try {
// Verify bot token
const botInfo = await this.bot.getMe();
logger.info(`Connected to Telegram as @${botInfo.username}`);
// Initialize channel monitoring
await this.channelMonitor.initialize(this.bot);
this.isConnected = true;
this.lastSync = new Date();
} catch (error) {
logger.error('Telegram connection failed:', error);
throw new ConnectionError('Telegram API connection failed', error);
}
}
async collectMentions(query: SocialQuery): Promise<SocialPost[]> {
try {
const posts: SocialPost[] = [];
// Get monitored channels
const channels = await this.channelMonitor.getActiveChannels();
for (const channel of channels) {
try {
const messages = await this.collectChannelMessages(channel, query);
posts.push(...messages);
} catch (error) {
logger.warn(`Failed to collect from channel ${channel.id}:`, error);
continue;
}
}
return posts;
} catch (error) {
logger.error('Telegram mention collection failed:', error);
throw new DataCollectionError('Telegram data collection failed', error);
}
}
private async collectChannelMessages(
channel: TelegramChannel,
query: SocialQuery
): Promise<SocialPost[]> {
const posts: SocialPost[] = [];
try {
// Calculate message limit based on timeframe
const timeDiffHours = (query.timeframe.end.getTime() - query.timeframe.start.getTime()) / (1000 * 60 * 60);
const messageLimit = Math.min(Math.max(Math.floor(timeDiffHours * 5), 10), 100); // 5 messages per hour, max 100
// Get recent messages
const messages = await this.bot.getChatHistory(channel.id, messageLimit);
for (const message of messages) {
// Check if message is within timeframe
if (!this.isMessageInTimeframe(message, query.timeframe)) {
continue;
}
// Check if message contains relevant keywords
if (!this.messageContainsKeywords(message, query)) {
continue;
}
const processedPost = await this.processTelegramMessage(message, channel);
posts.push(processedPost);
}
} catch (error) {
logger.error(`Channel message collection failed for ${channel.id}:`, error);
throw error;
}
return posts;
}
private async processTelegramMessage(
message: TelegramMessage,
channel: TelegramChannel
): Promise<SocialPost> {
// Extract user information
const author = await this.extractUserInfo(message.from);
// Process message content
const processedContent = this.messageProcessor.processContent(message.text || '');
// Extract entities
const entities = this.messageProcessor.extractEntities(message);
// Calculate engagement based on views and forwards
const engagement = this.calculateTelegramEngagement(message, channel);
return {
id: `tg_${channel.id}_${message.message_id}`,
platform: 'telegram',
content: processedContent.text,
author: {
id: author.id.toString(),
username: author.username || `user_${author.id}`,
displayName: author.first_name + (author.last_name ? ` ${author.last_name}` : ''),
verified: false, // Telegram doesn't have verification
followerCount: 0, // Not available
followingCount: 0, // Not available
authenticity: this.calculateTelegramUserAuthenticity(author)
},
timestamp: new Date(message.date * 1000),
metrics: {
likes: 0, // Not available in Telegram
shares: message.forward_from ? 1 : 0,
replies: 0, // Would need to track separately
views: message.views || 0,
engagement
},
entities,
metadata: {
channelId: channel.id,
channelTitle: channel.title,
messageId: message.message_id,
hasMedia: !!(message.photo || message.video || message.document),
isForwarded: !!message.forward_from
}
};
}
private calculateTelegramUserAuthenticity(user: TelegramUser): number {
let score = 0.5; // Base score
// Username presence (users with usernames are generally more authentic)
if (user.username) score += 0.2;
// Profile photo presence
if (user.photo) score += 0.1;
// Complete name (both first and last name)
if (user.first_name && user.last_name) score += 0.1;
// Bot detection (bots are less authentic for organic sentiment)
if (user.is_bot) score -= 0.3;
return Math.max(0, Math.min(1, score));
}
private calculateTelegramEngagement(message: TelegramMessage, channel: TelegramChannel): number {
if (!message.views || message.views === 0) return 0;
// Base engagement from views relative to channel size
const channelMembers = channel.member_count || 1000; // Default if unknown
const viewRatio = message.views / channelMembers;
let engagement = Math.min(viewRatio * 100, 100); // Cap at 100%
// Bonus for forwards
if (message.forward_from) engagement *= 1.2;
// Penalty for very old messages (engagement typically peaks early)
const messageAge = Date.now() - (message.date * 1000);
const hoursOld = messageAge / (1000 * 60 * 60);
if (hoursOld > 24) engagement *= 0.8; // 20% penalty after 24 hours
if (hoursOld > 168) engagement *= 0.5; // Additional penalty after 1 week
return Math.max(0, Math.min(100, engagement));
}
}Natural Language Processing Pipeline
Advanced Sentiment Analysis and Entity Recognition
// Comprehensive NLP pipeline for social media content analysis
export class SentimentAnalyzer {
private models: Map<string, SentimentModel> = new Map();
private entityExtractor: EntityExtractor;
private contextAnalyzer: ContextAnalyzer;
private cryptoLexicon: CryptoLexicon;
private languageDetector: LanguageDetector;
constructor(private config: SentimentConfig) {
this.entityExtractor = new EntityExtractor(config.entities);
this.contextAnalyzer = new ContextAnalyzer(config.context);
this.cryptoLexicon = new CryptoLexicon(config.lexicon);
this.languageDetector = new LanguageDetector();
this.initializeModels();
}
private async initializeModels() {
// Load pre-trained sentiment models
this.models.set('crypto_bert', await this.loadModel('crypto-bert-sentiment', {
modelPath: 'models/crypto-bert-sentiment',
tokenizer: 'crypto-bert-tokenizer',
maxLength: 512
}));
this.models.set('finbert', await this.loadModel('finbert-sentiment', {
modelPath: 'models/finbert',
tokenizer: 'finbert-tokenizer',
maxLength: 512
}));
this.models.set('general_sentiment', await this.loadModel('twitter-roberta-sentiment', {
modelPath: 'models/twitter-roberta-base-sentiment',
tokenizer: 'roberta-tokenizer',
maxLength: 280
}));
// Ensemble model for improved accuracy
this.models.set('ensemble', new EnsembleSentimentModel(
Array.from(this.models.values()),
{ weights: [0.4, 0.35, 0.25] } // Crypto-bert gets highest weight
));
}
async analyzeBatch(posts: SocialPost[]): Promise<SentimentAnalysisResult> {
try {
const results: PostSentimentResult[] = [];
const batchSize = 32; // Process in batches for efficiency
for (let i = 0; i < posts.length; i += batchSize) {
const batch = posts.slice(i, i + batchSize);
const batchResults = await this.processBatch(batch);
results.push(...batchResults);
}
// Calculate aggregate sentiment metrics
const aggregateMetrics = this.calculateAggregateMetrics(results);
// Detect sentiment manipulation
const manipulationScore = await this.detectSentimentManipulation(results, posts);
return {
overallSentiment: aggregateMetrics.weighted,
confidence: aggregateMetrics.confidence,
distribution: aggregateMetrics.distribution,
temporalTrends: this.analyzeTemporalTrends(results),
manipulationScore,
postResults: results,
metadata: {
totalPosts: posts.length,
processedPosts: results.length,
averageConfidence: aggregateMetrics.confidence,
languageDistribution: this.calculateLanguageDistribution(posts)
}
};
} catch (error) {
logger.error('Batch sentiment analysis failed:', error);
throw new SentimentAnalysisError('Sentiment analysis failed', error);
}
}
private async processBatch(posts: SocialPost[]): Promise<PostSentimentResult[]> {
const results: PostSentimentResult[] = [];
for (const post of posts) {
try {
const result = await this.analyzePost(post);
results.push(result);
} catch (error) {
logger.warn(`Failed to analyze post ${post.id}:`, error);
// Add neutral sentiment for failed analysis
results.push({
postId: post.id,
sentiment: 'neutral',
score: 0.5,
confidence: 0.1,
emotions: {},
entities: [],
explanation: 'Analysis failed'
});
}
}
return results;
}
private async analyzePost(post: SocialPost): Promise<PostSentimentResult> {
// Preprocess content
const preprocessedContent = this.preprocessContent(post.content);
// Detect language
const language = await this.languageDetector.detect(preprocessedContent);
if (language !== 'en') {
// For now, only process English content
return {
postId: post.id,
sentiment: 'neutral',
score: 0.5,
confidence: 0.1,
emotions: {},
entities: [],
explanation: `Non-English content (${language})`
};
}
// Extract entities
const entities = await this.entityExtractor.extract(preprocessedContent);
// Get sentiment predictions from all models
const modelPredictions: Map<string, SentimentPrediction> = new Map();
for (const [modelName, model] of this.models) {
if (modelName === 'ensemble') continue; // Skip ensemble for individual predictions
try {
const prediction = await model.predict(preprocessedContent);
modelPredictions.set(modelName, prediction);
} catch (error) {
logger.warn(`Model prediction failed: ${modelName}`, error);
}
}
// Get ensemble prediction
const ensembleModel = this.models.get('ensemble')!;
const ensemblePrediction = await ensembleModel.predict(preprocessedContent);
// Apply crypto-specific adjustments
const cryptoAdjustment = this.applyCryptoSentimentAdjustment(
preprocessedContent,
ensemblePrediction,
entities
);
// Analyze context and emotions
const contextAnalysis = await this.contextAnalyzer.analyze(preprocessedContent, post);
const emotions = this.extractEmotions(preprocessedContent, ensemblePrediction);
return {
postId: post.id,
sentiment: this.mapScoreToSentiment(cryptoAdjustment.score),
score: cryptoAdjustment.score,
confidence: cryptoAdjustment.confidence,
emotions,
entities: entities.map(e => ({
text: e.text,
type: e.type,
confidence: e.confidence,
sentiment: e.sentiment
})),
context: contextAnalysis,
modelPredictions: Object.fromEntries(modelPredictions),
explanation: cryptoAdjustment.explanation
};
}
private applyCryptoSentimentAdjustment(
content: string,
prediction: SentimentPrediction,
entities: ExtractedEntity[]
): CryptoSentimentResult {
let adjustedScore = prediction.score;
let confidence = prediction.confidence;
const adjustmentFactors: string[] = [];
// Check for crypto-specific positive indicators
const positiveIndicators = [
'moon', 'bullish', 'pump', 'gem', 'diamond hands', 'hodl',
'100x', 'to the moon', 'buy the dip', 'strong community',
'good fundamentals', 'solid project', 'great team'
];
// Check for crypto-specific negative indicators
const negativeIndicators = [
'rug pull', 'scam', 'dump', 'exit scam', 'honeypot',
'paper hands', 'dead project', 'abandoned', 'suspicious',
'red flag', 'avoid', 'warning', 'failed'
];
const contentLower = content.toLowerCase();
// Apply positive adjustments
for (const indicator of positiveIndicators) {
if (contentLower.includes(indicator)) {
adjustedScore = Math.min(1.0, adjustedScore + 0.1);
adjustmentFactors.push(`Positive: ${indicator}`);
confidence = Math.min(1.0, confidence + 0.05);
}
}
// Apply negative adjustments
for (const indicator of negativeIndicators) {
if (contentLower.includes(indicator)) {
adjustedScore = Math.max(0.0, adjustedScore - 0.2);
adjustmentFactors.push(`Negative: ${indicator}`);
confidence = Math.min(1.0, confidence + 0.1); // More confident about negative indicators
}
}
// Entity-based adjustments
for (const entity of entities) {
if (entity.type === 'CRYPTO_TOKEN' || entity.type === 'CONTRACT_ADDRESS') {
// Presence of crypto entities increases relevance and confidence
confidence = Math.min(1.0, confidence + 0.05);
if (entity.sentiment && Math.abs(entity.sentiment - 0.5) > 0.2) {
// Strong entity sentiment influences overall sentiment
const entityInfluence = (entity.sentiment - 0.5) * 0.1;
adjustedScore = Math.max(0, Math.min(1, adjustedScore + entityInfluence));
adjustmentFactors.push(`Entity influence: ${entity.text}`);
}
}
}
return {
score: adjustedScore,
confidence,
explanation: adjustmentFactors.length > 0
? `Crypto-adjusted: ${adjustmentFactors.join(', ')}`
: 'Standard sentiment analysis'
};
}
private extractEmotions(content: string, prediction: SentimentPrediction): EmotionScores {
// Simple emotion detection based on keywords and sentiment
const emotions: EmotionScores = {
joy: 0,
anger: 0,
fear: 0,
sadness: 0,
surprise: 0,
disgust: 0,
trust: 0,
anticipation: 0
};
const emotionKeywords = {
joy: ['happy', 'excited', 'great', 'awesome', 'amazing', 'love', 'moon'],
anger: ['angry', 'furious', 'mad', 'hate', 'terrible', 'awful'],
fear: ['scared', 'afraid', 'worried', 'nervous', 'risky', 'dangerous'],
sadness: ['sad', 'disappointed', 'depressed', 'crying', 'lost'],
surprise: ['surprised', 'shocked', 'unexpected', 'wow', 'incredible'],
disgust: ['disgusting', 'gross', 'sick', 'revolting'],
trust: ['trust', 'reliable', 'solid', 'confidence', 'believe'],
anticipation: ['excited', 'waiting', 'upcoming', 'soon', 'expecting']
};
const contentLower = content.toLowerCase();
for (const [emotion, keywords] of Object.entries(emotionKeywords)) {
let score = 0;
for (const keyword of keywords) {
if (contentLower.includes(keyword)) {
score += 0.2;
}
}
emotions[emotion as keyof EmotionScores] = Math.min(1, score);
}
return emotions;
}
private calculateAggregateMetrics(results: PostSentimentResult[]): AggregateMetrics {
if (results.length === 0) {
return {
weighted: 0.5,
simple: 0.5,
confidence: 0,
distribution: { positive: 0, neutral: 0, negative: 0 }
};
}
let weightedSum = 0;
let totalWeight = 0;
let positiveCount = 0;
let neutralCount = 0;
let negativeCount = 0;
let totalConfidence = 0;
for (const result of results) {
const weight = result.confidence; // Weight by confidence
weightedSum += result.score * weight;
totalWeight += weight;
totalConfidence += result.confidence;
// Count sentiment distribution
if (result.sentiment === 'positive') positiveCount++;
else if (result.sentiment === 'negative') negativeCount++;
else neutralCount++;
}
const weightedAverage = totalWeight > 0 ? weightedSum / totalWeight : 0.5;
const simpleAverage = results.reduce((sum, r) => sum + r.score, 0) / results.length;
const averageConfidence = totalConfidence / results.length;
return {
weighted: weightedAverage,
simple: simpleAverage,
confidence: averageConfidence,
distribution: {
positive: positiveCount / results.length,
neutral: neutralCount / results.length,
negative: negativeCount / results.length
}
};
}
}Manipulation Detection
Advanced Coordination and Shill Detection
// Sophisticated manipulation detection system
export class ManipulationDetector {
private shillDetector: ShillDetector;
private coordinationAnalyzer: CoordinationAnalyzer;
private botDetector: BotDetector;
private astroturfDetector: AstroturfDetector;
private networkAnalyzer: NetworkAnalyzer;
constructor(private config: ManipulationConfig) {
this.shillDetector = new ShillDetector(config.shill);
this.coordinationAnalyzer = new CoordinationAnalyzer(config.coordination);
this.botDetector = new BotDetector(config.bot);
this.astroturfDetector = new AstroturfDetector(config.astroturf);
this.networkAnalyzer = new NetworkAnalyzer(config.network);
}
async analyzeManipulation(posts: SocialPost[]): Promise<ManipulationAnalysisResult> {
try {
// Run all detection algorithms in parallel
const [
shillResult,
coordinationResult,
botResult,
astroturfResult,
networkResult
] = await Promise.all([
this.shillDetector.detectShilling(posts),
this.coordinationAnalyzer.detectCoordination(posts),
this.botDetector.detectBots(posts),
this.astroturfDetector.detectAstroturfing(posts),
this.networkAnalyzer.analyzeNetworkManipulation(posts)
]);
// Calculate overall manipulation score
const overallScore = this.calculateOverallManipulationScore([
shillResult,
coordinationResult,
botResult,
astroturfResult,
networkResult
]);
// Generate alerts and recommendations
const alerts = this.generateManipulationAlerts(overallScore, [
shillResult,
coordinationResult,
botResult,
astroturfResult,
networkResult
]);
return {
overallManipulationScore: overallScore.score,
confidence: overallScore.confidence,
riskLevel: this.categorizeRisk(overallScore.score),
detectionResults: {
shilling: shillResult,
coordination: coordinationResult,
botActivity: botResult,
astroturfing: astroturfResult,
networkManipulation: networkResult
},
alerts,
recommendations: this.generateRecommendations(overallScore.score, alerts),
metadata: {
totalPosts: posts.length,
analysisTimestamp: new Date().toISOString(),
algorithmsUsed: ['shill', 'coordination', 'bot', 'astroturf', 'network']
}
};
} catch (error) {
logger.error('Manipulation analysis failed:', error);
throw new ManipulationAnalysisError('Manipulation analysis failed', error);
}
}
private calculateOverallManipulationScore(results: DetectionResult[]): OverallScore {
const weights = {
shill: 0.25,
coordination: 0.25,
bot: 0.20,
astroturf: 0.15,
network: 0.15
};
let weightedScore = 0;
let totalWeight = 0;
let totalConfidence = 0;
for (let i = 0; i < results.length; i++) {
const result = results[i];
const weight = Object.values(weights)[i] * result.confidence;
weightedScore += result.score * weight;
totalWeight += weight;
totalConfidence += result.confidence;
}
return {
score: totalWeight > 0 ? weightedScore / totalWeight : 0,
confidence: totalConfidence / results.length
};
}
}
// Shill detection implementation
export class ShillDetector {
constructor(private config: ShillConfig) {}
async detectShilling(posts: SocialPost[]): Promise<ShillDetectionResult> {
try {
const suspiciousPatterns: ShillPattern[] = [];
let overallShillScore = 0;
// Group posts by author
const postsByAuthor = this.groupPostsByAuthor(posts);
// Analyze each author's posting patterns
for (const [authorId, authorPosts] of postsByAuthor) {
const authorAnalysis = await this.analyzeAuthorPattern(authorPosts);
if (authorAnalysis.isSuspicious) {
suspiciousPatterns.push({
type: 'suspicious_author',
authorId,
suspicionScore: authorAnalysis.suspicionScore,
evidence: authorAnalysis.evidence,
postCount: authorPosts.length
});
}
overallShillScore += authorAnalysis.suspicionScore * (authorPosts.length / posts.length);
}
// Detect content similarity patterns
const similarityPatterns = await this.detectContentSimilarity(posts);
suspiciousPatterns.push(...similarityPatterns);
// Detect timing patterns
const timingPatterns = await this.detectTimingPatterns(posts);
suspiciousPatterns.push(...timingPatterns);
// Calculate final shill score
const finalScore = Math.min(1, overallShillScore + (suspiciousPatterns.length * 0.1));
return {
score: finalScore,
confidence: this.calculateShillConfidence(suspiciousPatterns, posts.length),
suspiciousPatterns,
affectedPosts: this.getAffectedPosts(posts, suspiciousPatterns),
explanation: this.generateShillExplanation(suspiciousPatterns, finalScore)
};
} catch (error) {
logger.error('Shill detection failed:', error);
throw error;
}
}
private async analyzeAuthorPattern(posts: SocialPost[]): Promise<AuthorAnalysis> {
let suspicionScore = 0;
const evidence: string[] = [];
// Check posting frequency
const timeSpan = posts[posts.length - 1].timestamp.getTime() - posts[0].timestamp.getTime();
const hoursSpan = timeSpan / (1000 * 60 * 60);
const postsPerHour = posts.length / Math.max(hoursSpan, 1);
if (postsPerHour > 5) { // More than 5 posts per hour
suspicionScore += 0.3;
evidence.push(`High posting frequency: ${postsPerHour.toFixed(1)} posts/hour`);
}
// Check content repetition
const uniqueContent = new Set(posts.map(p => this.normalizeContent(p.content)));
const repetitionRatio = 1 - (uniqueContent.size / posts.length);
if (repetitionRatio > 0.5) { // More than 50% content repetition
suspicionScore += 0.4;
evidence.push(`High content repetition: ${(repetitionRatio * 100).toFixed(1)}%`);
}
// Check author authenticity
const author = posts[0].author;
if (author.authenticity < 0.3) {
suspicionScore += 0.2;
evidence.push('Low author authenticity score');
}
// Check for excessive promotional language
const promotionalScore = this.calculatePromotionalScore(posts);
if (promotionalScore > 0.7) {
suspicionScore += 0.3;
evidence.push('Excessive promotional language detected');
}
return {
isSuspicious: suspicionScore > 0.4,
suspicionScore: Math.min(1, suspicionScore),
evidence
};
}
private async detectContentSimilarity(posts: SocialPost[]): Promise<ShillPattern[]> {
const patterns: ShillPattern[] = [];
const similarityThreshold = 0.8;
// Calculate content similarity matrix
for (let i = 0; i < posts.length; i++) {
for (let j = i + 1; j < posts.length; j++) {
const similarity = this.calculateContentSimilarity(
posts[i].content,
posts[j].content
);
if (similarity > similarityThreshold) {
patterns.push({
type: 'content_similarity',
postIds: [posts[i].id, posts[j].id],
suspicionScore: similarity,
evidence: [`Content similarity: ${(similarity * 100).toFixed(1)}%`],
similarity
});
}
}
}
return patterns;
}
private calculateContentSimilarity(content1: string, content2: string): number {
// Implement Jaccard similarity for text content
const tokens1 = new Set(this.tokenize(content1.toLowerCase()));
const tokens2 = new Set(this.tokenize(content2.toLowerCase()));
const intersection = new Set([...tokens1].filter(x => tokens2.has(x)));
const union = new Set([...tokens1, ...tokens2]);
return intersection.size / union.size;
}
private tokenize(text: string): string[] {
return text.match(/\b\w+\b/g) || [];
}
private normalizeContent(content: string): string {
return content
.toLowerCase()
.replace(/[^\w\s]/g, '') // Remove punctuation
.replace(/\s+/g, ' ') // Normalize whitespace
.trim();
}
private calculatePromotionalScore(posts: SocialPost[]): number {
const promotionalKeywords = [
'buy now', 'don\'t miss', 'last chance', 'limited time',
'guaranteed', 'easy money', 'get rich', 'moon shot',
'next big thing', 'explosive growth', '1000x', 'lambo'
];
let totalScore = 0;
for (const post of posts) {
const contentLower = post.content.toLowerCase();
let postScore = 0;
for (const keyword of promotionalKeywords) {
if (contentLower.includes(keyword)) {
postScore += 0.1;
}
}
// Check for excessive use of emojis and capitalization
const emojiCount = (post.content.match(/[\u{1F600}-\u{1F64F}]|[\u{1F300}-\u{1F5FF}]|[\u{1F680}-\u{1F6FF}]|[\u{1F1E0}-\u{1F1FF}]/gu) || []).length;
const capsRatio = (post.content.match(/[A-Z]/g) || []).length / post.content.length;
if (emojiCount > 5) postScore += 0.1;
if (capsRatio > 0.3) postScore += 0.1;
totalScore += Math.min(1, postScore);
}
return totalScore / posts.length;
}
}
// Coordination detection implementation
export class CoordinationAnalyzer {
constructor(private config: CoordinationConfig) {}
async detectCoordination(posts: SocialPost[]): Promise<CoordinationDetectionResult> {
try {
const coordinationPatterns: CoordinationPattern[] = [];
// Temporal coordination detection
const temporalPatterns = await this.detectTemporalCoordination(posts);
coordinationPatterns.push(...temporalPatterns);
// Content coordination detection
const contentPatterns = await this.detectContentCoordination(posts);
coordinationPatterns.push(...contentPatterns);
// Network coordination detection
const networkPatterns = await this.detectNetworkCoordination(posts);
coordinationPatterns.push(...networkPatterns);
// Calculate overall coordination score
const coordinationScore = this.calculateCoordinationScore(coordinationPatterns);
return {
score: coordinationScore.score,
confidence: coordinationScore.confidence,
patterns: coordinationPatterns,
suspiciousGroups: this.identifySuspiciousGroups(coordinationPatterns),
explanation: this.generateCoordinationExplanation(coordinationPatterns)
};
} catch (error) {
logger.error('Coordination detection failed:', error);
throw error;
}
}
private async detectTemporalCoordination(posts: SocialPost[]): Promise<CoordinationPattern[]> {
const patterns: CoordinationPattern[] = [];
const timeWindow = 5 * 60 * 1000; // 5 minutes
const minimumPostsInWindow = 5;
// Sort posts by timestamp
const sortedPosts = [...posts].sort((a, b) => a.timestamp.getTime() - b.timestamp.getTime());
for (let i = 0; i < sortedPosts.length; i++) {
const windowStart = sortedPosts[i].timestamp.getTime();
const windowEnd = windowStart + timeWindow;
const postsInWindow = sortedPosts.slice(i).filter(post =>
post.timestamp.getTime() <= windowEnd
);
if (postsInWindow.length >= minimumPostsInWindow) {
// Check if these posts come from different authors
const uniqueAuthors = new Set(postsInWindow.map(p => p.author.id));
if (uniqueAuthors.size >= 3) { // At least 3 different authors
patterns.push({
type: 'temporal_coordination',
postIds: postsInWindow.map(p => p.id),
authorIds: Array.from(uniqueAuthors),
suspicionScore: Math.min(1, postsInWindow.length / 10), // Higher score for more posts
timeWindow: {
start: new Date(windowStart),
end: new Date(windowEnd)
},
evidence: [`${postsInWindow.length} posts from ${uniqueAuthors.size} authors in 5-minute window`]
});
}
}
}
return patterns;
}
private async detectContentCoordination(posts: SocialPost[]): Promise<CoordinationPattern[]> {
const patterns: CoordinationPattern[] = [];
const similarityThreshold = 0.7;
const minGroupSize = 3;
// Group posts by content similarity
const similarityGroups = this.groupBySimilarity(posts, similarityThreshold);
for (const group of similarityGroups) {
if (group.length >= minGroupSize) {
const uniqueAuthors = new Set(group.map(p => p.author.id));
if (uniqueAuthors.size >= 2) { // Multiple authors posting similar content
patterns.push({
type: 'content_coordination',
postIds: group.map(p => p.id),
authorIds: Array.from(uniqueAuthors),
suspicionScore: Math.min(1, group.length / 10),
evidence: [`${group.length} similar posts from ${uniqueAuthors.size} authors`],
similarity: this.calculateGroupSimilarity(group)
});
}
}
}
return patterns;
}
}Real-Time Social Monitoring
Live Social Media Streaming and Alert System
// Real-time social media monitoring and alert system
export class RealTimeSocialMonitor {
private streamManagers: Map<string, StreamManager> = new Map();
private alertEngine: SocialAlertEngine;
private trendDetector: TrendDetector;
private viralAnalyzer: ViralAnalyzer;
private eventEmitter: EventEmitter;
constructor(
private config: RealTimeMonitorConfig,
private socialCollectors: Map<string, SocialMediaCollector>,
private notificationService: NotificationService
) {
this.alertEngine = new SocialAlertEngine(config.alerts);
this.trendDetector = new TrendDetector(config.trends);
this.viralAnalyzer = new ViralAnalyzer(config.viral);
this.eventEmitter = new EventEmitter();
this.setupRealTimeStreaming();
}
private async setupRealTimeStreaming() {
for (const [platform, collector] of this.socialCollectors) {
try {
const streamManager = new StreamManager(platform, collector, {
bufferSize: this.config.bufferSize,
processingInterval: this.config.processingInterval,
alertThresholds: this.config.alertThresholds[platform]
});
await streamManager.initialize();
this.streamManagers.set(platform, streamManager);
// Set up event handlers
streamManager.on('newPost', (post) => this.handleNewPost(post));
streamManager.on('trendDetected', (trend) => this.handleTrendDetection(trend));
streamManager.on('viralContent', (content) => this.handleViralContent(content));
streamManager.on('suspiciousActivity', (activity) => this.handleSuspiciousActivity(activity));
logger.info(`Real-time streaming initialized for ${platform}`);
} catch (error) {
logger.error(`Failed to initialize streaming for ${platform}:`, error);
}
}
}
async monitorProject(projectId: string, monitoringConfig: ProjectMonitoringConfig): Promise<string> {
const monitoringId = `monitor_${projectId}_${Date.now()}`;
try {
// Create stream filters for the project
const streamFilters = this.createStreamFilters(monitoringConfig);
// Start monitoring on all platforms
for (const [platform, streamManager] of this.streamManagers) {
await streamManager.addProjectMonitoring(monitoringId, streamFilters);
}
// Set up project-specific alert rules
await this.alertEngine.addProjectAlerts(monitoringId, monitoringConfig.alertRules);
logger.info(`Started monitoring project ${projectId} with ID ${monitoringId}`);
return monitoringId;
} catch (error) {
logger.error(`Failed to start project monitoring for ${projectId}:`, error);
throw error;
}
}
private async handleNewPost(post: SocialPost) {
try {
// Process post through analysis pipeline
const analysis = await this.analyzePostRealTime(post);
// Check for alerts
const alerts = await this.alertEngine.checkPostAlerts(post, analysis);
// Emit events
this.eventEmitter.emit('postAnalyzed', { post, analysis, alerts });
// Send alerts if necessary
if (alerts.length > 0) {
await this.sendAlerts(alerts, post);
}
// Update trend detection
await this.trendDetector.updateWithPost(post, analysis);
} catch (error) {
logger.error('Failed to handle new post:', error, { postId: post.id });
}
}
private async analyzePostRealTime(post: SocialPost): Promise<RealTimeAnalysis> {
// Lightweight real-time analysis
const sentiment = await this.quickSentimentAnalysis(post.content);
const entities = await this.quickEntityExtraction(post.content);
const manipulation = await this.quickManipulationCheck(post);
return {
sentiment,
entities,
manipulation,
timestamp: new Date(),
confidence: this.calculateAnalysisConfidence(sentiment, entities, manipulation)
};
}
private async handleTrendDetection(trend: DetectedTrend) {
try {
logger.info(`Trend detected: ${trend.type}`, trend);
// Analyze trend significance
const significance = await this.trendDetector.analyzeTrendSignificance(trend);
if (significance.isSignificant) {
// Create trend alert
const trendAlert: TrendAlert = {
type: 'trend_detected',
trend,
significance,
timestamp: new Date(),
platforms: trend.platforms,
affectedProjects: trend.affectedProjects
};
// Send trend notifications
await this.notificationService.sendTrendAlert(trendAlert);
this.eventEmitter.emit('significantTrend', trendAlert);
}
} catch (error) {
logger.error('Failed to handle trend detection:', error);
}
}
private async handleViralContent(content: ViralContent) {
try {
logger.info(`Viral content detected: ${content.postId}`);
// Analyze viral spread pattern
const spreadAnalysis = await this.viralAnalyzer.analyzeSpread(content);
// Check for artificial amplification
const artificialScore = await this.viralAnalyzer.detectArtificialAmplification(content);
if (artificialScore > 0.7) {
// High probability of artificial amplification
const manipulationAlert: ManipulationAlert = {
type: 'artificial_viral',
content,
artificialScore,
spreadAnalysis,
timestamp: new Date()
};
await this.notificationService.sendManipulationAlert(manipulationAlert);
this.eventEmitter.emit('manipulationDetected', manipulationAlert);
}
} catch (error) {
logger.error('Failed to handle viral content:', error);
}
}
async generateRealTimeReport(timeframe: number = 3600000): Promise<RealTimeReport> {
const endTime = Date.now();
const startTime = endTime - timeframe;
try {
// Collect metrics from all stream managers
const platformMetrics: Record<string, PlatformMetrics> = {};
for (const [platform, streamManager] of this.streamManagers) {
platformMetrics[platform] = await streamManager.getMetrics(startTime, endTime);
}
// Get trend summary
const trendSummary = await this.trendDetector.getTrendSummary(startTime, endTime);
// Get viral content summary
const viralSummary = await this.viralAnalyzer.getViralSummary(startTime, endTime);
// Get alert summary
const alertSummary = await this.alertEngine.getAlertSummary(startTime, endTime);
return {
timeframe: { start: new Date(startTime), end: new Date(endTime) },
platformMetrics,
trendSummary,
viralSummary,
alertSummary,
overallHealth: this.calculateOverallSocialHealth(platformMetrics),
recommendations: this.generateRecommendations(alertSummary, trendSummary),
generatedAt: new Date()
};
} catch (error) {
logger.error('Failed to generate real-time report:', error);
throw error;
}
}
}
// Stream manager for individual platforms
export class StreamManager extends EventEmitter {
private activeStreams: Map<string, AsyncIterator<SocialPost>> = new Map();
private postBuffer: SocialPost[] = [];
private processingTimer: NodeJS.Timer | null = null;
private metrics: StreamMetrics;
constructor(
private platform: string,
private collector: SocialMediaCollector,
private config: StreamConfig
) {
super();
this.metrics = new StreamMetrics();
}
async initialize(): Promise<void> {
try {
// Start processing timer
this.processingTimer = setInterval(() => {
this.processBufferedPosts();
}, this.config.processingInterval);
logger.info(`Stream manager initialized for ${this.platform}`);
} catch (error) {
logger.error(`Stream manager initialization failed for ${this.platform}:`, error);
throw error;
}
}
async addProjectMonitoring(monitoringId: string, filters: StreamFilter[]): Promise<void> {
try {
// Create stream for project monitoring
const stream = await this.collector.streamRealTime(filters);
this.activeStreams.set(monitoringId, stream);
// Start consuming the stream
this.consumeStream(monitoringId, stream);
logger.info(`Added project monitoring ${monitoringId} for ${this.platform}`);
} catch (error) {
logger.error(`Failed to add project monitoring ${monitoringId}:`, error);
throw error;
}
}
private async consumeStream(monitoringId: string, stream: AsyncIterator<SocialPost>) {
try {
for await (const post of stream) {
// Add to buffer
this.postBuffer.push(post);
// Update metrics
this.metrics.recordPost(post);
// Check buffer size
if (this.postBuffer.length >= this.config.bufferSize) {
this.processBufferedPosts();
}
}
} catch (error) {
logger.error(`Stream consumption error for ${monitoringId}:`, error);
// Attempt to restart stream
setTimeout(() => {
this.restartMonitoring(monitoringId);
}, 5000);
}
}
private async processBufferedPosts() {
if (this.postBuffer.length === 0) return;
const postsToProcess = this.postBuffer.splice(0);
try {
for (const post of postsToProcess) {
// Emit new post event
this.emit('newPost', post);
// Check for trends
const trendCheck = await this.checkForTrends(post, postsToProcess);
if (trendCheck.isTrending) {
this.emit('trendDetected', trendCheck.trend);
}
// Check for viral content
const viralCheck = await this.checkForViralContent(post);
if (viralCheck.isViral) {
this.emit('viralContent', viralCheck.content);
}
// Check for suspicious activity
const suspiciousCheck = await this.checkForSuspiciousActivity(post, postsToProcess);
if (suspiciousCheck.isSuspicious) {
this.emit('suspiciousActivity', suspiciousCheck.activity);
}
}
} catch (error) {
logger.error('Failed to process buffered posts:', error);
}
}
async getMetrics(startTime: number, endTime: number): Promise<PlatformMetrics> {
return this.metrics.getMetrics(startTime, endTime);
}
}This comprehensive Social Intelligence Agent documentation provides the technical foundation for understanding and implementing sophisticated social media monitoring, sentiment analysis, and manipulation detection capabilities that power Kaizen AI's social intelligence features.
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