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Data Streaming and Artificial Intelligence: The Future of Real-Time Social Media Monitoring

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Artificial intelligence (AI) is playing an increasingly important role in social media. It’s helping to improve the user experience, provide better customer support, and help businesses make more informed decisions. The list of AI-driven applications in social media is continually growing and already includes:

  • Personalized content delivery: AI algorithms can analyze user data to deliver personalized content to users, such as news articles, product recommendations, and advertisements.

  • Content moderation: AI algorithms can automatically detect and remove inappropriate content, such as hate speech, fake news, and spam.

  • Chatbots: AI-powered chatbots can interact with users on social media platforms and provide customer support, answer questions, and offer personalized recommendations.

  • Sentiment analysis: AI-powered sentiment analysis tools can analyze social media data to understand how people feel about a particular topic, brand, or product.

  • Influencer marketing: AI algorithms can analyze social media data to identify influencers who have a large following and engage with their audience.

Another area in which AI is being used is brand management.  As social media adoption has skyrocketed over the past 20 years, brand management has fundamentally changed. Now, individual posts from customers or employees can quickly become viral, sometimes boosting reputations, but also risking significant damage to both brand reputation and company share price.   

To limit this potential damage, many organizations have turned to AI powered by data streaming. By implementing an event-driven architecture, companies can stream publicly available data from across different social media platforms in real time and rely on AI models to identify potentially damaging posts before they become materially significant. This allows PR teams to respond in a timely manner and effectively mitigate risks to brand reputation. 

In this blog, we’ll give an overview of how this can be done with Confluent—the leading data streaming platform based on Apache Kafka. We’ll start by covering some high-level technical considerations about streaming from social media, before delving into how to implement a real-time brand reputation monitoring system with AI on Confluent Cloud. 

Brand Management on Social Media: Technical Considerations

At a general technical level, these are the three fundamental steps (and their associated challenges) in creating a system that can help flag potentially damaging social posts:

1. Data extraction process: Initiate a data collection request across a range of social media platforms. The volume of data acquired will be contingent upon the accessibility of individual posts.

Technical Challenges: The massive and often unpredictable volume of data generated on social media platforms can strain the capabilities of data collection systems. Scalability becomes crucial to handle fluctuations in data influx without compromising performance. In addition, storing and managing large amounts of data is resource intensive.

2. Data Analysis: The collected data must undergo processing to assess the various types of information acquired during the data extraction procedure. Utilizing tailor-made AI analysis engines can be employed to enhance the precision of flagging social media posts as needed.

Technical Challenges: During the analysis phase, addressing two critical issues becomes paramount: data formats and data quality. Social media data is often diverse and unstructured data coming in various formats including text, images, videos, and more. Furthermore, textual data includes different forms of data like captions, hashtags, emojis, and abbreviations. Data from different sources might contain spam, irrelevant content, inconsistencies, or contradictory information, which may affect the accuracy of the analysis.

3. Result Generation: Scores derived from analyzed data are aggregated and lead to the generation of outcomes based on the amassed score.

Technical Challenges: Aggregating these diverse outputs coherently can be challenging. Analyzing data from various sources and platforms makes it difficult to standardize and combine results effectively. Some data sources might carry more weight than others, which requires them to be calibrated in order to arrive at a result.

Real-Time Brand Reputation Monitoring with Confluent

Confluent is a leading data streaming platform offering managed Apache Kafka and has revolutionized the way organizations handle data in real time. Based on the Kora engine, Confluent’s capabilities can be leveraged to create an efficient social media monitoring process. 

Data ingestion: Confluent’s support for Java and non-Java clients and pre-built connectors, including HTTP Source Connectors, facilitates the seamless extraction of data from social media APIs. This ensures reliable and consistent data collection from various platforms. 

For this blog, we are leveraging a Python client hosted on AWS Lambda to extract data from an Instagram (public account) load into Kafka topics. For data consistency, all media files are loaded into a S3 bucket and the URI reference to the file location is updated to the Kafka topic.

######Set Environment Variables
instagram_username=',userid'
s3_bucket=<bucket>
access_key=""
secret_key=""
s3 = boto3.resource('s3',region_name=<region>,aws_access_key_id=access_key,aws_secret_access_key=secret_key)

######Request data from Instagram
class GetInstagramProfile():
   def __init__(self) -> None:
       self.L = instaloader.Instaloader()
​
   def get_user_information(self,username):
       profile = instaloader.Profile.from_username(self.L.context, username)
       info={}
       info['username']=profile.username
       info['userid']=profile.userid
       info["number_of_posts"]= profile.mediacount
       info["followers_count"]= profile.followers
       info["following_count"]= profile.followees
       info["bio"]=profile.biography
       info["external_url"]=profile.external_url
       info=json.dumps(info)
       print(info)
       producer.produce("user_basic_info", key=username, value=info)

######Load media files into S3 bucket and upload URI to Kafka Topics
image_files = [file for file in os.listdir("./"+username+"/") if file.endswith(('.jpg'))]
       for image in image_files:
           path=""
           path="./"+username+"/"+image
           s3.Bucket(s3_bucket).upload_file(Filename=path, Key=username+'_'+image)
           s3_path=username+'_'+image
           info={}
           info['username'] = username
           info['s3_path'] = s3_path
           info['bucket']=s3_bucket
           info=json.dumps(info)
           print(info)
           producer.produce("user_posts_images", key=s3_path, value=info)

Event sourcing and data flow: By ingesting data into Kafka topics, you establish an event sourcing pipeline, which serves as the central data hub ensuring that data flows consistently through various stages of analysis. Kafka’s pub-sub model minimizes interdependencies among data sources, expediting data analysis by triggering numerous downstream operations. Its ability to deliver data in real time ensures that downstream operations receive the latest data at the earliest.

######Media Analysis using AWS Rekognition

def lambda_handler(event, context):
​
   client = boto3.client("rekognition")
   x=json.dumps(event[0])
   x=json.loads(x)
   x=x['payload']
   x=x['value']
   x=x.replace('=','":"').replace(', ','","').replace('{','{"').replace('}','"}')
   x=json.loads(x)
   print(x)

   #passing s3 image data path to rekognition
   response = client.detect_moderation_labels(Image={"S3Object": {"Bucket": x['bucket'],"Name": x['s3_path']}}, MinConfidence=50)
   x['rekognition_output']=response['ModerationLabels']
   x['ModerationLabels']=[]
   for label in response['ModerationLabels']:
       try:
           x['ModerationLabels'].append(label['Name'])
       except:
           pass
​
   return x

Aggregation and result generation: Aggregating weighted scores allows you to generate informed final results. Real-time streaming tools like Kafka Streams, KSQL, or Flink empower you to handle dynamic data manipulation and aggregation. We are leveraging KSQL to aggregate scores from different analyses and generate the final results.

######KSQL CODE BLOCK
create table final_output as select user_info.username,user_info.number_of_posts,user_info.followers_count,user_info.following_count,user_info.bio,
text_output.present_keywords, text_output.captions_score, rekognition_output.ModerationLabels,rekognition_output.username as user_name from user_info
inner join text_output on user_info.username=text_output.username
inner join rekognition_output on user_info.username=rekognition_output.username emit changes;

Final results are then sent to S3 storage for publication and long-term persistence. 

Below is the diagram of the Confluent Cloud architecture built for this monitoring process.

This use case relies on the following features of Confluent Cloud: 

  • Kora Engine – Confluent Cloud is built on Kora–the cloud-native engine for Apache Kafka®. Powered by Kora engine, Confluent has delivered GBs/sec for enterprise organizations, scaling seamlessly with increased demand. 

  • Pre-built connectors – Confluent offers a library of over 120 pre-built connectors, 70 of which are fully managed. 

  • Stream Processing – Confluent enables organizations to filter, join, or aggregate streams of social media data in real time, facilitating its use for AI.

Protecting Your Brand With Confluent And AI

With the rise of social media, reputation management has become both more important and more complex. PR teams must be alert to potentially damaging posts, 24/7, across multiple social media platforms; missing just one could spell significant losses for their organizations. 

Real-time social media monitoring powered by Confluent reduces the risk of this happening. With a real-time stream of data from across social media platforms feeding AI algorithms, PR teams can stay vigilant to developing threats and take action to mitigate their negative impacts. 

With elastic scalability, fully managed connectors, and in-flight stream processing, Confluent provides the backbone of a reliable reputation management system. 

Ready to try Confluent Cloud? Try now, for free.

  • Mureli Srinivasa Ragavan is a Senior Solutions Engineer at Confluent with over 17 years of industry experience. Mureli is a trusted advisor to his customers, providing them with architectural strategy, technology adoption guidance, and continuous engagement model. Throughout his career, Mureli has built a reputation for his ability to create and implement innovative solutions that drive business growth and success.

    His deep understanding of cloud solutions, combined with his experience in working with Kafka and Confluent Cloud, has enabled him to build strong relationships with his clients and help them achieve their strategic goals. In his free time, Mureli enjoys exploring new technologies and fidgeting with his camera.

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