Unlock the Power of BERT for Binary Text Classification

In the world of natural language processing, one of the most important tasks is binary text classification. Binary text classification is the process of classifying text into two distinct classes. For example, a binary classifier could classify an email into either spam or not.  A new deep learning tool called BERT (Bidirectional Encoder Representations from…… Continue reading Unlock the Power of BERT for Binary Text Classification

Towards Designing a More Inclusive Technology

In Human-Computer Interaction (HCI) research, awareness of how to address diversity is increasing. For example, the number of published papers that offer information on different diversity dimensions has significantly increased [2]. Designers and developers aim to be aware of the different types of their technology users to ensure that no individual or community is harmed…… Continue reading Towards Designing a More Inclusive Technology

Cross-Platform Social Media Studies: Five Advantages And Five Disadvantages

There is a growing body of research on social networks, including studies that focus on how social networks are used on different platforms, how users move between platforms, and how platforms are integrated into the overall media diet of users. A study by the Pew Research Center found that social networks are used by most…… Continue reading Cross-Platform Social Media Studies: Five Advantages And Five Disadvantages

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

Core Elements of a Web Analytics Process

Here is a description of a web analytics process with just the core stages. The core of a Web analytics process consists of four essential stages, shown below. Collecting data: At this stage, you collect the basic, elementary data. Typically, these data are counts of things or events. Processing of data into information: At this…… Continue reading Core Elements of a Web Analytics Process

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

Big Data Fallacy

Big Data Fallacy. The law of large numbers argues that the sample’s mean approaches the sample population’s actual average as a sample size increases. This concept is often, either implicitly or explicitly, taken as a justification as to why ‘big data’ (i.e., millions or billions of samples) cannot be wrong. However, there are contrary arguments and…… Continue reading Big Data Fallacy

Flaw of Averages

When working with numbers representing many people, one often employs the average to describe the people represented by the numbers. However, this approach can lead to serious problems due to the flaw of averages, which is that findings based on the average are wrong on average. In fact, often when dealing with people, the average person…… Continue reading Flaw of Averages