Which engagement level can we predict better on Instagram? A classification approach

In social media, likes and comments are key metrics for success.  If you can predict how many likes and comments a post will get, you can better manage your content strategy and create content that resonates with your audience.  There are a few ways to build a prediction model for the number of likes.  In…… Continue reading Which engagement level can we predict better on Instagram? A classification approach

Demystifying Language Models: The Case of BERT’s Usage in Solving Classification Problems

Newcomers to the field of Artificial Intelligence (AI) often see the term ‘language model’ tossed around when discussing Natural Language Processing (NLP) tasks without any proper clarification of its importance and usage in solving real-world problems. So, this tutorial blogpost aims at demystifying language models by defining what a language model is, describing the common…… Continue reading Demystifying Language Models: The Case of BERT’s Usage in Solving Classification Problems

Log Odds Ratio: Going Beyond Simple Term Frequencies to Characterize Textual Categories

Gaining insights from text-based data can be a daunting task, even when the data is labeled with ground truth categories and ready for usage in machine learning tasks.Researchers often rely on simple methods like the frequency of words in each category to understand the collection’s characteristics. However, this approach is not always insightful, as term…… Continue reading Log Odds Ratio: Going Beyond Simple Term Frequencies to Characterize Textual Categories

VLCP: A four-level framework for social media user engagement metrics

When evaluating the performance of social media content, user engagement is a key performance indicator (KPI). Specifically, the interaction by the user with the content, such as viewing, liking, commenting, and sharing. From the mindset of a social media content producer, users with more public expressiveness of their interest have higher impactful engagement with the…… Continue reading VLCP: A four-level framework for social media user engagement metrics

The Illusion of Data Validity: Why Numbers About People Are Likely Wrong

This reflection article addresses a difficulty faced by scholars and practitioners working with numbers about people, which is that those who study people want numerical data about these people. Unfortunately, time and time again, this numerical data about people is wrong. Addressing the potential causes of this wrongness, we present examples of analyzing people numbers,…… Continue reading The Illusion of Data Validity: Why Numbers About People Are Likely Wrong

When mere correlations are not enough: The Granger Causality test

In most data science-related problems, datasets consist of multiple variables, in which independent variables might depend on other independent variables. When the variables in datasets represent observations at different times, we call this dataset a time series set. The time interval in these data sets may be hourly, daily, weekly, monthly, quarterly, annually, etc. One…… Continue reading When mere correlations are not enough: The Granger Causality test

SegmentSizeEstimator, a research tool of the Acua Platform

Wondering what factors contribute to high levels of online engagement? In our research, we have found that one of the most reliable predictors of level of engagement for an ad, online content, or social media post for a given channel is simply size of the target population. For example, we’ve ranked viewers of YouTube channels…… Continue reading SegmentSizeEstimator, a research tool of the Acua Platform

Engineers, Aware! Commercial Tools Disagree on Social Media Sentiment

For segmentation, one often need to use sentiment analysis services. Large commercial sentiment analysis tools are often deployed in software engineering due to their ease of use. However, it is not known how accurate these tools are, and whether the sentiment ratings given by one tool agree with those given by another tool. We use…… Continue reading Engineers, Aware! Commercial Tools Disagree on Social Media Sentiment

What Really Matters?: Characterizing and Predicting User Engagement of News Postings Using Multiple Platforms, Sentiments, and Topics

This research characterizes user engagement of approximately 3,000,000 news postings of 53 news outlets and 50,000,000 associated user comments during 8 months on 5 social media platforms (i.e. Facebook, Instagram, Twitter, YouTube, and Reddit). We investigate the effect of sentiments and topics on user engagement across four levels of user engagement expressions (i.e. views, likes,…… Continue reading What Really Matters?: Characterizing and Predicting User Engagement of News Postings Using Multiple Platforms, Sentiments, and Topics

Measuring 9 emotions of news posts from 8 news organizations across 4 social media platforms for 8 months

Using Plutchik’s wheel of emotions framework, we identify the emotional content of 133,487 social media posts and the audience’s emotional engagement expressed in 2,824,162 comments on those posts. We measure nine emotions (anger, anticipation, anxiety, disgust, joy, fear, sadness, surprise, trust) and two sentiments (positive and negative) using two extraction resources (EmoLex, LIWC) for eight…… Continue reading Measuring 9 emotions of news posts from 8 news organizations across 4 social media platforms for 8 months