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

Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

Artificial intelligence, particularly machine learning, carries high potential to automatically detect customers’ pain points, which is a particular concern the customer expresses that the company can address. However, unstructured data scattered across social media make detection a nontrivial task. Thus, to help firms gain deeper insights into customers’ pain points, the authors experiment with and…… Continue reading Detecting Pain Points from User-Generated Social Media Posts Using Machine Learning

Measuring user interactions with websites: A comparison of two industry standard analytics approaches using data of 86 websites

Measuring user interactions with websites: A comparison of two industry standard analytics approaches using data of 86 websitesThis research compares four standard analytics metrics from Google Analytics with SimilarWeb using one year’s average monthly data for 86 websites from 26 countries and 19 industry verticals. The results show statistically significant differences between the two services…… Continue reading Measuring user interactions with websites: A comparison of two industry standard analytics approaches using data of 86 websites

SiloSolver: Algorithm for Aggregating Siloed Customer Segments in Facebook Ads Campaigns

Data silo problem refers to related datasets located in different databases, systems, or files. In the case of online advertising, performance metrics are fragmented in different campaigns and ad sets, making it difficult to compare customer segments. In this research, we present SiloSolver, an algorithm that: (a) retrieves performance metrics for different customer segments across…… Continue reading SiloSolver: Algorithm for Aggregating Siloed Customer Segments in Facebook Ads Campaigns

Next Likely Behavior: Predicting Individual Actions from Aggregate User Behaviors

We report results using n-grams to model user actions with only aggregated data and knowing little about the user. Employing a data set of 33,860 flight bookings from 4,221 passengers, we evaluate the n-gram model for the precision of predicting next likely actions. Results show that our approach can achieve a precision of 21% overall…… Continue reading Next Likely Behavior: Predicting Individual Actions from Aggregate User Behaviors

Manual and Automatic Methods for User Needs Detection in Requirements Engineering: Key Concepts and Challenges

User needs inform designers and developers of essential functionalities for requirements engineering. In this work, we summarize key concepts and challenges relating to manual and automatic user needs detection methods. We discuss six challenges with manual and eight challenges with automated methods. A. Challenges of Manual Methods • A01: Limited sample sizes. • A02: Budgetary…… Continue reading Manual and Automatic Methods for User Needs Detection in Requirements Engineering: Key Concepts and Challenges

Performance analysis of keyword advertising campaign using gender-brand effect of search queries

In this research, we analyze the relationship among (1) the performance metrics of a sponsored search campaign, (2) the gender orientation of queries, and (3) the occurrence of branded terms in queries. The aim of this research is to investigate the effectiveness of increased personalization of search engine advertising in order to improve the consumer’s…… Continue reading Performance analysis of keyword advertising campaign using gender-brand effect of search queries