A Web Scraping of Chemical Compounds with an Anti-Drug Feature Using IoT

As a result of the COVID-19 epidemic, there has been an increase in the demand for electronic education apps in schools and colleges in recent years. The purpose of this paper is to develop an educational application for chemistry students at different levels of study that will allow them to obtain precise information on chemical substances in a timely and safe manner. This program uses a web scraping technique by applied a  RESRful API to extract information from websites, which is then sent to the student's account. Furthermore, due to the use of the Internet of Things, the application  has an anti-explosives and narcotics property using (IOT). The application can retrieve and save information entered by the user on chemical compounds with a high level of security. The medication's chemical formula, as well as the covalent and ionic bonding of compounds, can be displayed. It also has a database that lists all of the hazardous substances. If a user enters a dangerous compound containing narcotics more than four times, an alarm message is sent to the administrator via the Internet of Things

Digital methods in a post-API environment

Qualitative and mixed methods digital social research often relies on gathering and storing social media data through the use of APIs (Application Programming Interfaces). In past years this has been relatively simple, with academic developers and researchers using APIs to access data and produce visualisations and analysis of social networks and issues. In recent years, API access has become increasingly restricted and regulated by corporations at the helm of social media networks. Facebook (the corporation) has restricted academic research access to Facebook (the social media platform) along with Instagram (a Facebook-owned social media platform). Instead, they have allowed access to sources where monetisation can easily occur, in particular, marketers and advertisers. This leaves academic researchers of digital social life in a difficult situation where API related research has been curtailed. In this paper we describe some rationales and methodologies for using APIs in social research. We then introduce some of the major events in academic API use that have led to the prohibitive situation researchers now find themselves in. Finally, we discuss the methodological and ethical issues this produces for researchers and, suggest some possible steps forward for API related research

Tutorial: Legality and Ethics of Web Scraping

Researchers and practitioners often use various tools and technologies to automatically retrieve data from the Web (often referred to as Web scraping) when conducting their projects. Unfortunately, they often overlook the legality and ethics of using these tools to collect data. Failure to pay due attention to these aspects of Web Scraping can result in serious ethical controversies and lawsuits. Accordingly, we review legal literature together with the literature on ethics and privacy to identify broad areas of concern together with a list of specific questions that researchers and practitioners engaged in Web scraping need to address. Reflecting on these questions and concerns can potentially help researchers and practitioners decrease the likelihood of ethical and legal controversies in their work

TWITTER POLICING

Police departments are frequently utilizing social media platforms to actively interact with the public. Social media offers an opportunity to share information, facilitate communication, and foster stronger connections between police departments and the communities they serve. In this context sentiment analysis of social media data has become a tool, for identifying sentiments and tracking emerging trends. This project utilizes sentiment analysis to examine the social media interactions with particular data obtained from the Twitter (X). Initially, the project gathers social media data, from twitter mentioned accounts on Twitter utilizing web scraping techniques. Afterwards, we perform a thorough sentiment analysis using techniques, in Natural Language Processing (NLP). We utilize two reliable sentiment analysis tools, TextBlob and Natural Language Toolkit (NTLK) to classify media posts into three distinct sentiment categories positive, negative, and neutral. Additionally, it monitors the opinions of public sentiments on a monthly basis. The project utilizes data visualization techniques like pie charts, line charts, and clustered column charts to represent sentiment analysis data in appealing ways that highlight the distribution and the trends of public opinion

SISTEM PREDIKSI KEPRIBADIAN MANUSIA BERDASARKAN STATUS MEDIA SOSIAL MENGGUNAKAN SUPPORT VECTOR MACHINE

Currently, social media is a forum for exchanging information widely used by the public, such as Facebook and Twitter. Social media users exchange information to find out the condition of one another. Some companies use social media to explore the personality potential of prospective employees to be recruited. However, to dig up this information takes a very long time because the company has to open prospective employees' social media one by one. To dig up information automatically, a personality detection system is needed from social media users. This study develops a person's personality prediction system based on social media status using the support vector machine. The data sets evaluated in this study were 300 Facebook social media status data and 2067 Twitter social media status data. Based on the evaluation results, we obtained a high level of accuracy in detecting a person's personality based on social media status, namely 100% for Facebook user status and 99.3% for Twitter user status.Keywords: Personality, Social Media, Support Vector Machine, Facebook, Twitter ABSTRAKSaat ini, media sosial merupakan salah suatu wadah pertukaran informasi yang banyak digunakan oleh masyarakat, seperti Facebook maupun Twitter. Pengguna media sosial saling bertukar informasi untuk mengetahui kondisi satu dengan lainnya. Beberapa perusahaan memanfaatkan media sosial untuk menggali potensi kepribadian dari calon pegawai yang akan direkrut. Namun, untuk menggali informasi tersebut memerlukan waktu yang sangat lama karena perusahan harus membuka media sosial dari calon pegawai satu per satu. Agar dapat menggali informasi secara otomatis, maka diperlukan sistem deteksi kepribadian dari pengguna media sosial. Penelitian ini mengembangkan sistem prediksi kepribadian seseorang berdasarkan status media sosial menggunakan metode Support Vector Machine. Set data yang dievaluasi dalam penelitian ini yaitu 300 data status media sosial Facebook dan 2067 data status media sosial Twitter. Berdasarkan hasil evaluasi yang dilakukan diperoleh tingkat akurasi yang tinggi dalam mendeteksi kepribadian seseorang berdasarkan status media sosial, yaitu 100% untuk status pengguna Facebook dan 99,3% untuk status pengguna Twitter.  Kata Kunci: Kepribadian, Media Sosial, Support Vector Machine, Facebook,  Twitter

Russo-Ukrainian War: Prediction and explanation of Twitter suspension

On 24 February 2022, Russia invaded Ukraine, starting what is now known as the Russo-Ukrainian War, initiating an online discourse on social media. Twitter as one of the most popular SNs, with an open and democratic character, enables a transparent discussion among its large user base. Unfortunately, this often leads to Twitter's policy violations, propaganda, abusive actions, civil integrity violation, and consequently to user accounts' suspension and deletion. This study focuses on the Twitter suspension mechanism and the analysis of shared content and features of the user accounts that may lead to this. Toward this goal, we have obtained a dataset containing 107.7M tweets, originating from 9.8 million users, using Twitter API. We extract the categories of shared content of the suspended accounts and explain their characteristics, through the extraction of text embeddings in junction with cosine similarity clustering. Our results reveal scam campaigns taking advantage of trending topics regarding the Russia-Ukrainian conflict for Bitcoin and Ethereum fraud, spam, and advertisement campaigns. Additionally, we apply a machine learning methodology including a SHapley Additive explainability model to understand and explain how user accounts get suspended

Perbandingan Metode Klasifikasi Random Forest dan SVM Pada Analisis Sentimen PSBB

COVID-19 in Indonesia, has made the local government not remain silent. Several local governments in Indonesia have enacted regulations to reduce the growth of COVID-19 victims by limiting public meetings with Large-Scale Social Restrictions or LSSR. However, the implementation of this LSSR has received many comments from social media users, especially from Twitter. This research was conducted with the aim of analyzing the sentiment of implementing the LSSR with media tweets on the Twitter social media platform. The data that were successfully extracted were 466 tweet data with training data and test data having a ratio of 7 to 3. Then the data was calculated into 2 different algorithms to be compared, the first algorithm used was the Support Vector Machine (SVM) algorithm and Random Forest with the aim get the most accurate sentiment analysis results

The Disinformation Landscape and the Lockdown of Social Platforms

This introduction to the special issue considers how independent research on mis/disinformation campaigns can be conducted in a corporate environment hostile to academic research. We provide an overview of the disinformation landscape in the wake of the Facebook–Cambridge Analytica data scandal and social platforms’ decision to enforce access lockdowns and the throttling of Application Programming Interfaces (APIs) for data collection. We argue that the governance shift from user communities to social media algorithms, along with social platforms’ intensive emphasis on generating revenue from user data, has eroded the mutual trust of networked publics and opened the way for dis/misinformation campaigns. We discuss the importance of open, public APIs for academic research as well as the unique challenges of collecting social media data to study highly ephemeral mis/disinformation campaigns. The introduction concludes with an assessment of the growing data access gap that not only hinders research of public interest, but that may also preclude researchers from identifying meaningful research questions as activity on social platforms becomes increasingly more inscrutable and unobservable