Keywords: public Opinion, Social media, Big data, software

Social media platforms have evolved since the 1990s, emerging as key spaces for users to create, share, and participate in content. With the rise of the World Wide Web (WWW) in the early 2000s, social media became an integral part of the digital landscape, allowing individuals to engage in real-time discussions and express opinions on various topics. Public opinion, defined as the collective views of individuals on societal issues, especially regarding voting intentions, has long been measured through traditional methods such as surveys, face-to-face interactions, and public meetings. However, these methods are often not enough to capture the full spectrum of public sentiment, as they can be subject to biases, limited access, and emotional influences.
The rapid expansion of internet usage in the 2000s marked a shift, merging public opinion with the virtual sphere. Social media emerged as a dynamic channel for the formation and diffusion of public opinion. Platforms, alongside influencers and other digital tools, began to play a pivotal role in shaping public perception and providing insights into societal trends. In this context, social media has increasingly been referred to as a "social sensor," enabling the prediction of political, economic, and social phenomena. This shift from data scarcity to data abundance presents both opportunities and challenges for the study of public opinion.
The advantages of social media in measuring public opinion are numerous: (1) social media analytics offer unprecedented insights into public behavior, (2) researchers can access diverse conversations and networks across time and geographic boundaries, (3) the volume of user-generated content allows for comprehensive sentiment analysis, (4) tracking opinion dynamics and information diffusion over time enhances our understanding of public sentiment, and (5) social media enables continuous public opinion tracking.
However, several challenges persist: (1) the data from social media platforms is often incomplete or fragmented, (2) the data is noisy and unstructured, (3) algorithms may introduce biases, and (4) repeated interactions can distort the representation of public opinion.
This research aims to explore the analytical tools and software available for studying social media as a means of capturing and measuring public opinion. It will also address how to effectively manage the large volumes of data generated by social media platforms and how to address the biases and limitations inherent in digital public opinion measurement. Through this exploration, the study aims to contribute to the understanding of how computational social science can offer new ways to measure and analyze public opinion in the digital age.