Emplifi Listening helps you get a 360° view of everything that touches your brand. To ensure you get what you need from your data, Listening covers the most crucial listening metrics.

Before diving into the metrics, it's important to define and mention a few things first:

• Listening Mentions - Listening mentions are the individual pieces of content discovered by the Listening feature and shown in the results.
• Data Sources Available & Coverage - All the information about data types, sources, and coverage in Listening, is available in our knowledge base here.
• Data Retention: In Analytics and Content modules, the listening mentions and their data are stored for 12 months. In Community, data is stored for 90 days.
  

Due to Facebook and Instagram API restrictions, all the metrics below include only publicly available data. This means that private data such as insights, ads, etc. are not available.

Listening Metrics

You can go through the listening metrics in the Listening tab of the Analytics module. The Available Views offer the following metrics:

• Age (Twitter only) - The metric informs about the age of the authors of the mentions in the analyzed queries.
  • The demographics are based on public data from Twitter. It’s unavailable for Facebook and Instagram.
    

• Age & Gender (Twitter only) - The metric is a breakdown of the age and gender of the authors who created the mentions in the analyzed queries.
  • The demographics are based on public data from Twitter. It’s unavailable for Facebook and Instagram.

• Country - The metric informs about the countries from where the majority of your listening mentions are coming. The country is defined by the location of the author of the mention.
  • For Twitter, the country is defined by the geolocation of the device of the mention’s author. When the geolocation is not available, the country is assigned to the profile automatically by Emplifi’s AI tool or manually by our research team. 
  • For Facebook and Instagram, the geolocation is assigned to the profiles automatically by Emplifi’s AI tool or manually by our research team.
  • Please note that the country is assigned to an extensive amount of social media profiles, but still not all of them.

• Content Overview - The most frequently used keywords, emojis, and hashtags visualized in a word cloud. The more frequent the entity is in the mentions, the larger it is in the word cloud.

• Facebook Mentions - The number of content pieces that come from Facebook and match the analyzed queries. Mentions discovered by multiple analyzed queries are counted only once.

• Gender (Twitter only) - The metric informs about the gender of the authors of the mentions in the analyzed queries.
  • The demographics are based on public data from Twitter. It’s unavailable for Facebook and Instagram.

• Instagram Mentions - The number of content pieces that come from Instagram and match the analyzed queries. Mentions discovered by multiple analyzed queries are counted only once.

• Interests - The interests inform about what’s being discussed in the mentions in the analyzed queries. Compared to topics, interests are less specific. You can think of interests as rather general areas that can be specified by topics.
  • One listening mention can contain multiple interests.
  • The interests are detected from the text of the listening mention using a machine learning-based system that supports the following languages: Arabic, Czech, German, English, Spanish, French, Indonesian, Korean, Portuguese, Russian. The system can predict 320 different interests.
    

• Key Metrics Summary - A chart comparing the total number of mentions, authors, social interactions, and potential impressions.
  

• Language - The most frequently used languages detected in the mentions in the analyzed queries.
  • The language is detected by our language detector. More details on how the detector works are available here.
  • Please note that in some cases, the language detected by Twitter can differ from the language detected by Emplifi and occasionally, the analytics may display a different set of languages than you would expect.
    

• Number of Interactions by Label - The number of interactions on the mentions matching the analyzed queries broken down by a label manually assigned to them in the Content or Community module. Mentions labeled with multiple labels are counted towards those multiple labels.
  
  
• Number of Mentions - The number of content pieces matching the analyzed queries. Mentions discovered by multiple analyzed queries are counted only once.
  
  
• Number of Mentions by Label - The number of content pieces matching the analyzed queries broken down by a label manually assigned to them in the Content or Community module.
  • Mentions labeled with multiple labels are counted towards those multiple labels.
    
    
• Negative Mentions - An aggregation of mentions with a negative sentiment.
  More information about our Sentiment Analysis is available here.
  
  
• Positive & Negative Keywords - The word cloud sums up frequently used keywords found in the mentions with positive and negative sentiment. It’s a mix of typical positive and typical negative keywords.
  • More information about our Sentiment Analysis is available here.
    
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• Positive Mentions - An aggregation of mentions with a positive sentiment.
  More information about our Sentiment Analysis is available here.
  
  
• Potential Impressions - (Twitter only) Potential impressions of a listening mention inform about how many people may see the mention. The number is equal to the number of followers the mention’s author had at the time of publishing the mention.
  The overall number of potential impressions of the analyzed queries is the sum of the potential impressions of all the individual listening mentions. If an author produced more than one mention in the analyzed queries, their followers are multiplied by the number of mentions they produced.
  • The Potential impressions metric is derived from Twitter’s number of followers metric. It’s unavailable for Facebook and Instagram.
    

• Potential Impressions by Label - (Twitter only) The number of impressions on the mentions matching the analyzed queries broken down by a label manually assigned to them in the Content or Community module. Mentions labeled with multiple labels are counted towards those multiple labels.
  • The Potential impressions metric is derived from Twitter’s number of followers metric. It’s unavailable for Facebook and Instagram.

• Professions - (Twitter only) Top professions of the authors who created the mentions in the analyzed queries.
  • The professions are based on public data from Twitter and derive from the job titles detected in the bios of Twitter accounts. We support 160 job title groups.
    

• Sentiment of Mentions - The classification of the listening mentions based on the sentiment of their content. If no sentiment is applied to the mention, we don’t include it in the chart. More information about our Sentiment Analysis is available here.
  The sentiment can be: 
  • Positive
  • Negative
  • Neutral

• Social Interactions - The number of interactions on the mentions matching the analyzed queries.
  
  
• Top Authors by Mentions - List of authors arranged by the number of mentions they've produced.
  Authors who can’t be identified due to API limitations (mostly authors of mentions coming from Instagram hashtag search) are displayed as Unknown profiles. Such profiles are listed with only one mention since there's no way to aggregate the mentions to a unique author.
  
  
• Top Authors by Potential Impressions - A list of authors arranged by the number of potential impressions detects the authors who may potentially have the biggest impact on the audience.
  • The list typically includes only Twitter authors because the metric is derived from Twitter’s number of followers, which is unavailable for Facebook and Instagram. If there are no or too few Twitter profiles, other social media can appear.
  • Authors who can’t be identified due to API limitations (mostly authors of mentions coming from Instagram hashtag search) are displayed as Unknown profiles. Such profiles are listed with only one mention since there's no way to aggregate the mentions to a unique author.
    
    
• Top Emojis - The most frequently used emojis in all the content of the analyzed queries.
  
  
• Top Events - The most frequently mentioned events in all the content of the analyzed queries.
  The top events are based on the detection and recognition model for the entities mentioned in the text of the listening mentions.
  • These models process the text and detect the group of words that represent entities of different types. These types include people, organizations, locations, and in some cases, dates, numerals, etc.
    
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• Top Hashtags - The most frequently used hashtags in all the content of the analyzed queries.
  
  
• Top Keywords - The most frequently used keywords in all the content of the analyzed queries.
  
  
• Top Organizations - The most frequently mentioned organizations in all the content of the analyzed queries.
  The top organizations are based on the detection and recognition model for the entities mentioned in the text of the listening mentions.
  • These models process the text and detect the group of words that represent entities of different types. These types include people, organizations, locations, and in some cases, dates, numerals, etc.
    
    
• Top Persons - The most frequently mentioned people in all the content of the analyzed queries.
  The top persons are based on the detection and recognition model for the entities mentioned in the text of the listening mentions.
  • These models process the text and detect the group of words that represent entities of different types. These types include people, organizations, locations, and in some cases, dates, numerals, etc.
    
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• Top Places - The most frequently mentioned places in all the content of the analyzed queries.
  The top places are based on the detection and recognition model for the entities mentioned in the text of the listening mentions.
  • These models process the text and detect the group of words that represent entities of different types. These types include people, organizations, locations, and in some cases, dates, numerals, etc.
    
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• Top URLs - A list of URLs that are most frequently attached to the mentions in the analyzed queries.

• Twitter Mentions - The number of content pieces that come from Twitter and match the analyzed queries. Mentions discovered by multiple analyzed queries are counted only once.

• Typical Positive Keywords - The word cloud sums up frequently used keywords found in the mentions with a positive sentiment.
  • More information about our Sentiment Analysis is available here.
    
    
• Typical Negative Keywords - The word cloud sums up frequently used keywords found in the mentions with negative sentiment.
  • More information about our Sentiment Analysis is available here.
    
    
• Typical Positive Topics - Positive topics inform about what’s being discussed in the mentions with positive sentiment (more about Sentiment Analysis here).
  • One listening mention can contain multiple topics.
  • Topics are detected from the text of the listening mention using a machine learning-based system that predicts 10 thousand different topics. It supports the following languages: English, Spanish, Portuguese, and partially Czech.
    
    
• Typical Negative Topics - Negative topics inform about what’s being discussed in the mentions with negative sentiment (more about Sentiment Analysis here).
  • One listening mention can contain multiple topics.
  • Topics are detected from the text of the listening mention using a machine learning-based system that predicts 10 thousand different topics. It supports the following languages: English, Spanish, Portuguese, and partially Czech.