Twitter Trending Topics Research Articles

Topic Sentiment Analysis for Twitter Data in Indian Languages Using Composite Kernel SVM and Deep Learning

Sentiment analysis of public opinions on social networks, such as Twitter or Facebook, can provide us with valuable information, which has a wide range of applications. But the efficiency and accuracy of the automated methods for Twitter sentiment analysis are hindered by the special characteristics of the Twitter data. The Twitter data is generally noisy, high-dimensional, and it has complex syntactic and semantic structures. Sentiment analysis of Twitter data in Indian languages is more challenging because the data is multilingual and code-mixed. In this article, we propose various composite kernel functions, each of which is used with Support Vector Machines (SVM) for developing a model for topic sentiment analysis of Twitter data in Indian languages. Each composite kernel function is constructed by taking the weighted summation of multiple single kernel functions defined by us. In addition to our proposed composite kernel SVM method, we use several state-of-the-art deep learning classifiers for topic sentiment classification. Since any suitable Twitter dataset in Indian languages is not available for conducting our experiments, we have developed our own datasets by collecting tweets related to five different Twitter trending topics in India. To prove the robustness and generalization capability of the proposed models, they are also evaluated on the US airline Twitter dataset which is a publicly available benchmark English dataset. The empirical study exhibits that the proposed composite kernel SVM method is effective for the sentiment classification task. In the case of Indian language datasets, the proposed composite kernel SVM method achieves the highest average accuracy of 74% and the highest average F-score of 0.73. On the other hand, the deep learning-based method achieves the average accuracy and the average F-score of 71.31% and 0.70, respectively. In the case of the US airline Twitter dataset, the proposed composite kernel SVM method achieves the average accuracy of 83% and the average F-score of 0.82, which are higher than that of the deep learning-based method.

The Representation of Society Criticism on Meme (Semiotic Analysis of Charles Sanders Pierce on Politician Billboard Memes)

This research aims to discover the meaning of three previously selected memes that highlight the practice of placing billboards for Indonesian politicians outside of the 2024 presidential election campaign using semiotic analysis from Charles Sanders Pierce with a descriptive qualitative approach. The three memes that are the subject of this research are three memes that have a high virality value and are included in Twitter's Trending Topics. The findings of this research show that the three memes have a negative attitude against the action of placing political billboards. These negative viewpoints are summed up in four key points that emerge from the three memes, namely (1) Criticism of installing billboards for politicians that are excessive in number and placement, (2) Criticism of the unimportance of installing billboards by politicians compared to other issues that are also being viral in the world. Twitter, (3) Criticism of the lack of empathy of politicians towards people who are experiencing economic difficulties in the midst of the second wave of the Covid-19 pandemic and (4) Criticism of politicians who only want to advertise themselves. In addition, the use of signs in the memes studied uses general signs that are close to the Indonesian people as well as things that are going viral close to the viral billboard phenomenon, such as The Beatles Album Cover, Tinder Dating App Logo, footballer Lionel Messi.

Social Media Analytics as a Tool for Cultural Spaces—The Case of Twitter Trending Topics

We are entering an era in which online personalities and personas will grow faster and faster. People are tending to use the Internet, and social media especially, more frequently and for a wider variety of purposes. In parallel, a number of cultural spaces have already decided to invest in marketing and message spreading through the web and the media. Growing their audience, or locating the appropriate group of people to share their information, remains a tedious task within the chaotic environment of the Internet. The investment is mainly financial—usually large—and directed to advertisements. Still, there is much space for research and investment in analytics that can provide evidence considering the spreading of the word and finding groups of people interested in specific information or trending topics and influencers. In this paper, we present a part of a national project that aims to perform an analysis of Twitter’s trending topics. The main scope of the analysis is to provide a basic ordering on the topics based on their “importance”. Based on this, we clarify how cultural institutions can benefit from such an analysis in order to empower their online presence.

Query-oriented topical influential users detection for top-k trending topics in twitter

Query-oriented topical influential users detection for top-k trending topics in twitter

Discovery and Classification of Twitter Bots

Online social networks (OSN) are used by millions of users, daily. This user-base shares and discovers different opinions on popular topics. The social influence of large groups may be affected by user beliefs or be attracted by the interest in particular news or products. A large number of users, gathered in a single group or number of followers, increases the probability to influence more OSN users. Botnets, collections of automated accounts controlled by a single agent, are a common mechanism for exerting maximum influence. Botnets may be used to better infiltrate the social graph over time and create an illusion of community behaviour, amplifying their message and increasing persuasion. This paper investigates Twitter botnets, their behavior, their interaction with user communities, and their evolution over time. We analyze a dense crawl of a subset of Twitter traffic, amounting to nearly all interactions by Greek-speaking Twitter users for a period of 36 months. The collected users are labeled as botnets, based on long-term and frequent content similarity events. We detect over a million events, where seemingly unrelated accounts tweeted nearly identical content, at almost the same time. We filter these concurrent content injection events and detect a set of 1850 accounts that repeatedly exhibit this pattern of behavior, suggesting that they are fully or in part controlled and orchestrated by the same entity. We find botnets that appear for brief intervals and disappear, as well as botnets that evolve and grow, spanning the duration of our dataset. We analyze the statistical differences between the bot accounts and the human users, as well as the botnet interactions with the user communities and the Twitter trending topics.

Making up Audience: Media Bots and the Falsification of the Public Sphere

ABSTRACT The purpose of this paper is to discuss if and how Brazilian media outlets make use of automated strategies and artificial intelligence (AI) in order to produce convenient social media metrics about themselves and amplify their relevance on Twitter. We examine how media bots can manipulate online ratings, change social perception of what is relevant and increase engagement with both on- and offline media entities. We extracted three types of data: (i) 530,942 tweets containing at least one URL from Globo Group or Folha Group collected via Twitter API; (ii) URL metadata from 158,690 articles by Globo Group and Folha Group; (iii) Twitter trending topics in Brazil. Profiles that posted links were later sampled and classified using the Botometer. Automated and human accounts were analyzed regarding their posting frequency and speed. In this paper, we assess the hypothesis that the existence of media bots is affecting the Twittersphere in Brazil, where automated accounts, empowered by AI, might be responsible for a substantial share of the links to popular Brazilian media outlets on Twitter. Our research provides quantitative empirical evidence that bots are particularly active in amplifying news media links in the initial moments of spreading. Additionally, automated accounts play an important role in promoting TV broadcast programs in Brazil. Based on these evidences, we discuss the strategies adopted by Brazilian media corporations to sustain their omnipresence online that boosts their online audience.

Identify Potential Breaking News Based on Social Media Posts Using Machine Learning

Social media consists of interactive applications and platforms for creating and exchange of user-generated contents. Due to its ease of use, speed and reach, social media is changing the public discourse in society and setting trends and agendas in topics that range from the healthcare and educational industry to politics and the technology. In recent days, the use of Social Media (Twitter) has tremendously increased. People are 24x7 available on Social Media and post and share a lot of data. The aim and goal of this project is to use the data from Social Media for predicting the news from it and calling for help in crisis. Sometimes it happens like there is a disaster or tragedy in some area and no emergency services are called. The proposed work classifies Twitter Trending Topics into categories such as disease, healthcare and calamities. Implementation conducted on twitter dataset shows effectiveness of the proposed approach.

Determining Trending Topics in Twitter with a Data-Streaming Method in R

Trending topics in Twitter is a collection of certain topics that are widely discussed by users. This study aims to design a model and strategy for finding trending topics from data streams on Twitter. The research approach was carried out in four stages, namely twitter data collection, preprocessing data, data analysis with sequential K-Means clustering and information processing. Sequential K-Means is used because it can receive input data sequentially and the cluster center can be updated. Testing of the model is carried out in three scenarios where each scenario is distinguished between the amount of data, time and parameter values. After that, evaluation of the results of clustering will be done using the Dunn Index method. Trending topics twitter application were created using the R language and produce output in the form of histograms. There are five topics being the trending topics in New York before the new year. The topic of "Times" relates to the presence of a new year's celebration night concert in Times Square. The "Hours" topic deals with the calculation of time and seconds towards 2017. "Eve" and "Party" topics relate to celebrations and the topic "Resolution" relating to hope and change for New Yorkers in in 2017.

Trending topics detection of Indonesian tweets using BN-grams and Doc-p

Researches on trending topics detection, especially on Twitter, have increased and various methods for detecting trending topics have been developed. Most of these researches have been focused on tweets written in English. Previous researches on trending topics detection on Indonesian tweets are still relatively few. In this paper, we compare two methods, namely document pivot and BN-grams, for detecting trending topics on Indonesian tweets. In our experiments, we examine the effects of varying the number of topics, n-grams, stemming, and aggregation on the quality of the resulting trending topics. We measure the accuracy of trending topics detection by comparing both algorithms with trending topics found in local news and Twitter trending topics. The results of our experiments show that using ten topics produces the highest topic recall; that using trigrams in BN-grams results in the highest value topic recall; and that using aggregation reduces the quality of trending topics produced. Overall, BN-grams has a higher value of topic recall than that of document pivot.

Studying the controversy in online crowds’ interactions

Detecting controversy in OSN can be very beneficial in understanding the behavior of the online crowds and the dynamics of their interactions, which leads to better understanding of what moves them and how they can be influenced. In this study, we present a hybrid approach that benefits from structural and content-based approaches to detect controversies in Twitter's trending topics. Unlike many previous works, we do not limit ourselves to a certain domain. However, we do focus on social content written in Arabic. To the best of our knowledge, very limited attention has been invested to undertake this approach in studying controversy in general topics in this region of the world. We collect tweets on different trending topics from different domains. We apply several approaches for controversy detection and compare their outcomes to determine which one is the best.

The attention automaton: Sensing collective user interests in social network communities

The vast quantity of information shared in social networks has brought us to an age of attention scarcity, where getting users to be attentive to a message is not a given. In fact, it has become the limiting factor in the consumption of information by end users. Understanding what captures the collective attention within a community of users in a social network is invaluable to many applications, such as product marketing, targeted advertising and social or political campaign organization. Several scholars have analyzed how information spreads in social networks under the constraint of attention. However, few papers provide a quantitative method to model and predict attention at every instant in the dynamic social web. In this paper, we propose the Attention Automaton , a probabilistic finite automata that can estimate the collective attention of some user community. Communities are based on geographical vicinity of users or having common interests (like followers of a given account) on Twitter. We identify two key factors that drive collective user attention: (1) the attention volatility of the community (frequency of change of trending topics), and (2) the selective categorical affinity of the user group towards certain trends. Our results, which are based on a eight-month dataset of Twitter trending topics across 111 geographic regions and audience trends of approximately 50 brands indicate that the proposed Attention Automaton can predict audience reception of impending trends based on categorical filters and inherent oscillations in user activity.

Clustering memes in social media streams

The problem of clustering content in social media has pervasive applications, including the identification of discussion topics, event detection, and content recommendation. Here we describe a streaming framework for online detection and clustering of memes in social media, specifically Twitter. A pre-clustering procedure, namely protomeme detection, first isolates atomic tokens of information carried by the tweets. Protomemes are thereafter aggregated, based on multiple similarity measures, to obtain memes as cohesive groups of tweets reflecting actual concepts or topics of discussion. The clustering algorithm takes into account various dimensions of the data and metadata, including natural language, the social network, and the patterns of information diffusion. As a result, our system can build clusters of semantically, structurally, and topically related tweets. The clustering process is based on a variant of Online K-means that incorporates a memory mechanism, used to "forget" old memes and replace them over time with the new ones. The evaluation of our framework is carried out by using a dataset of Twitter trending topics. Over a one-week period, we systematically determined whether our algorithm was able to recover the trending hashtags. We show that the proposed method outperforms baseline algorithms that only use content features, as well as a state-of-the-art event detection method that assumes full knowledge of the underlying follower network. We finally show that our online learning framework is flexible, due to its independence of the adopted clustering algorithm, and best suited to work in a streaming scenario.

트위터 트랜딩 토픽을 이용한 HBase 기반 자동 요약 시스템

Twitter has been a popular social media platform where people post short messages of 140 characters or less via the web. A hashtag is a word or acronym created by Twitter users to open a discussion about certain topics and issues that have a very high percentage of trending. Since the hashtag posts are sorted by time, not relevancy, people who firstly use Twitter have had difficulty understanding their context. In this paper, we propose a HBase-based automatic summary system in order to reduce the difficulty of understanding. The proposed system combines an automatic summary method with a fuzzy system after storing the streaming data provided by Twitter API to the HBase. Throughout this procedure, we have eliminated the duplicate of contents in the hashtag posts and have computed scores between posts so that the users can access to the trending topics with relevancy. ☞ keyword : Twitter trending topics, Automatic summary system, Fuzzy theory, HBase, NoSQL, Twitter API

Sequential Summarization: A Full View of Twitter Trending Topics

As an information delivering platform, Twitter collects millions of tweets every day. However, some users, especially new users, often find it difficult to understand trending topics in Twitter when confronting the overwhelming and unorganized tweets. Existing work has attempted to provide a short snippet to explain a topic, but this only provides limited benefits and cannot satisfy the users' expectations. In this paper, we propose a new summarization task, namely sequential summarization, which aims to provide a serial of chronologically ordered short sub-summaries for a trending topic in order to provide a complete story about the development of the topic while retaining the order of information presentation. Different from the traditional summarization task, the numbers of sub-summaries for different topics are not fixed. Two approaches, i.e., stream-based and semantic-based approaches, are developed to detect the important subtopics within a trending topic. Then a short sub-summary is generated for each subtopic. In addition, we propose three new measures to evaluate the position-aware coverage, sequential novelty and sequence correlation of the system-generated summaries. The experimental results based on the proposed evaluation criteria have demonstrated the effectiveness of the proposed approaches.

Gatekeeping Twitter: message diffusion in political hashtags

This article explores the structure of gatekeeping in Twitter by means of a statistical analysis of the political hashtags #FreeIran, #FreeVenezuela and #Jan25, each of which reached the top position in Twitter Trending Topics. We performed a statistical correlation analysis on nine variables of the dataset to evaluate if message replication in Twitter political hashtags was correlated with network topology. Our results suggest an alternative scenario to the dominant view regarding gatekeeping in Twitter political hashtags. Instead of depending on hubs that act as gatekeepers, we found that the intense activity of individuals with relatively few connections is capable of generating highly replicated messages that contributed to Trending Topics without relying on the activity of user hubs. The results support the thesis of social consensus through the influence of committed minorities, which states that a prevailing majority opinion in a population can be rapidly reversed by a small fraction of randomly distributed committed agents.

Real time discovery of dense clusters in highly dynamic graphs

Due to their real time nature, microblog streams are a rich source of dynamic information, for example, about emerging events . Existing techniques for discovering such events from a microblog stream in real time (such as Twitter trending topics), have several lacunae when used for discovering emerging events; extant graph based event detection techniques are not practical in microblog settings due to their complexity; and conventional techniques, which have been developed for blogs, web-pages, etc., involving the use of keyword search, are only useful for finding information about known events. Hence, in this paper, we present techniques to discover events that are unraveling in microblog message streams in real time so that such events can be reported as soon as they occur. We model the problem as discovering dense clusters in highly dynamic graphs. Despite many recent advances in graph analysis, ours is the first technique to identify dense clusters in massive and highly dynamic graphs in real time. Given the characteristics of microblog streams, in order to find clusters without missing any events, we propose and exploit a novel graph property which we call short-cycle property . Our algorithms find these clusters efficiently in spite of rapid changes to the microblog streams. Further we present a novel ranking function to identify the important events. Besides proving the correctness of our algorithms we show their practical utility by evaluating them using real world microblog data. These demonstrate our technique's ability to discover, with high precision and recall, emerging events in high intensity data streams in real time. Many recent web applications create data which can be represented as massive dynamic graphs. Our technique can be easily extended to discover, in real time, interesting patterns in such graphs.