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Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment
Abstract
Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text analysis software, we conducted a contentanalysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is indeed used extensively for political deliberation. We find that the mere number of messages mentioning a party reflects the election result. Moreover, joint mentions of two parties are in line with real world political ties and coalitions. An analysis of the tweets' political sentiment demonstrates close correspondence to the parties' and politicians' political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape. We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research. Copyright © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
... Two studies have emerged as highly significant in the realm of leveraging social media content for the purpose of forecasting political elections. The research conducted [22] examines the German election of 2009. The study involved collecting and analysing tweets that mentioned any of the six political parties represented in the German parliament, as well as their prominent Members of Parliament (MPs). ...
... Chung et al. [98] attempted to compare and contrast the lexicon-based election prediction techniques employed from two separate experiments [22] and [23]. Both approaches were implemented on a new dataset that includes users' tweets on the 2010 US Senate special election for this research. ...
... Both approaches were implemented on a new dataset that includes users' tweets on the 2010 US Senate special election for this research. The new dataset yielded drastically different findings when Tumasjan A. et al. approach [22] was applied to it, demonstrating that a candidate's proportion of mentions is insufficient for election predictions. As a result, Chung employed the lexicon approach proposed by O'Connor B. et al. [23] to find associations between the terms. ...
Modern social media’s rise to prominence has altered the ways in which candidates reach out to voters and conduct campaigns. Researchers often dwell upon the uses of social media platforms as a plethora of information for various tasks, such as election prediction, since they contain a large volume of people’s ideas about politics and leaders. Modern political campaigns and party propaganda make extensive use of social media. It is common practise for political parties and candidates to utilise Twitter and other social media during election season for coverage and promotion. This study analyses and provides estimates for the reliability of several volumetric social media techniques to predict election outcomes from social media activity. Incredibly large datasets made available by social media sites may be mined for insights into societal problems and predictions about the future. However, this is difficult because of the skewed and noisy nature of the data. This literature review aims to enlighten readers about the researchers’ input towards the process of forecasting election outcomes using social media content by outlining an assessment of sentiment analysis and its methodologies. The study also discusses research that aims to foretell upcoming elections in several nations by analysing user textual data on social media sites. In addition, this paper has pointed out some of the research gaps that exist in the area of election outcome forecasting and some of the challenging questions in the domain of sentiment analysis. In addition, this paper makes recommendations for the future of election prediction based on material gleaned from social media.
... Users utilize these platforms to express their opinions and thoughts about events and topics, allowing for the analysis of the communication around a certain event by leveraging these data to predict a certain event based on user opinions. These opinions have been shared by many researchers who have utilized social media data with machine learning techniques to make predictions about the future (Asur & Huberman, 2010;Bermingham & Smeaton, 2011;Jaidka et al., 2019;Kim et al., 2021;Kumpulainen et al., 2020;Makazhanov & Rafiei, 2013;Tumasjan et al., 2010). Moreover, researchers are constantly trying to improve the prediction models. ...
... A popular approach in research where social media data are leveraged to predict the outcome of an event is by mining the sentiment on social media platforms from user-generated content (Shahheidari et al., 2013). This technique has previously been employed to forecast the outcome of several political elections (Tumasjan et al., 2010;Nawaz et al., 2022). Prior research by Tumasjan et al. (2010), where a content analysis on Tweets related to political parties or candidates around the German Federal Election in 2009, was conducted, and voting behavior based on the frequency of Tweets mentioning a party and their sentiment was predictedboth studies showed that Twitter data can reflect the results of an election as well as that of the sentiment of Tweets, and the volume of positive Tweets can be leveraged to predict the outcome of political events. ...
... This technique has previously been employed to forecast the outcome of several political elections (Tumasjan et al., 2010;Nawaz et al., 2022). Prior research by Tumasjan et al. (2010), where a content analysis on Tweets related to political parties or candidates around the German Federal Election in 2009, was conducted, and voting behavior based on the frequency of Tweets mentioning a party and their sentiment was predictedboth studies showed that Twitter data can reflect the results of an election as well as that of the sentiment of Tweets, and the volume of positive Tweets can be leveraged to predict the outcome of political events. These results inspired further studies in this field (Asur & Huberman, 2010;Bermingham & Smeaton, 2011;Makazhanov & Rafiei, 2013), of which some did not support their notion of social media data being a reliable basis for predicting political events because of the incorrect prediction of results (Jungherr et al., 2012). ...
As per agenda-setting theory, political agenda is concerned with the government’s agenda, including politicians and political parties. Political actors utilize various channels to set their political agenda, including social media platforms such as Twitter (now X). Political agenda-setting can be influenced by anonymous user-generated content following the Bright Internet. This is why speech acts, experts, users with affiliations and parties through annotated Tweets were analyzed in this study. In doing so, the agenda formation during the 2019 European Parliament Election in Germany based on the agenda-setting theory as our theoretical framework, was analyzed. A prediction model was trained to predict users’ voting tendencies based on three feature categories: social, network, and text. By combining features from all categories logistical regression leads to the best predictions matching the election results. The contribution to theory is an approach to identify agenda formation based on our novel variables. For practice, a novel approach is presented to forecast the winner of events.
... In summary, a sentiment aggregate function calculates a global value based on the number of positive, negative, and neutral mentions of each political target, in a given period. We conducted an exhaustive study and collected and implemented several sentiment aggregate functions from the state of the art [4,[6][7][8][9][10][11][12][13]. ...
... One avenue of research that has been explored in recent years concerns the use of social media to predict present and future political events, namely electoral results [4,[6][7][8][9][10][11][12][13]. Although there is no consensus about methods and their consistency [21,22]. ...
... Defending the same idea, Diakopoulos el al. [27] studied the global sentiment variation based on Twitter messages of an Obama vs McCain political TV debate while it was still happening. Tumasjan et al. [10] used Twitter data to predict the 2009 Federal Election in Germany. They stated that "the mere number of party mentions accurately reflects the election result". ...
The automatic content analysis of mass media in the social sciences has become necessary and possible with the raise of social media and computational power. One particularly promising avenue of research concerns the use of sentiment analysis in microblog streams. However, one of the main challenges consists in aggregating sentiment polarity in a timely fashion that can be fed to the prediction method. We investigated a large set of sentiment aggregate functions and performed a regression analysis using political opinion polls as gold standard. Our dataset contains nearly 233 000 tweets, classified according to their polarity (positive, negative or neutral), regarding the five main Portuguese political leaders during the Portuguese bailout (2011-2014). Results show that different sentiment aggregate functions exhibit different feature importance over time while the error keeps almost unchanged.
... One of the fundamental research problems in the field of social media group polarization is its measurement. Early measurement methods based on statistical approaches suffered from issues such as overly simplistic for the complexity of social media dynamics (Bilal et al., 2019;Gaurav et al., 2013;Hart et al., 2020;Jaidka et al., 2018;Tumasjan et al., 2010). Current mainstream methods, such as text clustering or sentiment classification (Belcastro et al., 2020;Jiang et al., 2018;Ribeiro et al., 2017;Tyagi et al., 2020), struggle to balance efficiency and interpretability. ...
... In current trend of exploring group polarization via social media, volume-based schemes focus more on various data metrics and employ statistical methods in research. Notable examples include Gaurav et al.'s political polarization study based on the moving average aggregate probability method, Tumasjan et al.'s analysis of political polarization using the LIWC tool, and Hart et al.'s use of multidimensional statistical analysis to analyse polarization during COVID-19 (Gaurav et al., 2013;Tumasjan et al., 2010;Hart et al., 2020). ...
Introduction. Group polarization is an important research direction in social media content analysis, attracting many researchers to explore this field. Therefore, how to effectively measure group polarization has become a critical topic. Measuring group polarization on social media presents several challenges that have not yet been addressed by existing solutions. First, social media group polarization measurement involves processing vast amounts of text, which poses a significant challenge for information extraction. Second, social media texts often contain hard-to-understand content, including sarcasm, emojis, and internet slang. Additionally, group polarization research focuses on holistic analysis, while texts is typically fragmented. These challenges indicate that a new solution needs to be proposed. Method. To address these challenges, we designed a solution based on a multi-agent system and used a graph-structured community sentiment network (CSN) to represent polarization states. Furthermore, we developed a metric called community opposition index (COI) based on the CSN to quantify polarization. Experiments & Conclusion. We tested our multi-agent system through a zero-shot stance detection task and achieved outstanding results, which proved its significant value in terms of usability and accuracy.
... In this sense, some researchers examined the influence of operating distinct financial resources to improve forecasting accuracy through deep learning approaches (Day and Lee, 2016). However, sentiment analysis of tweets can provide additional information about the market directions that can help investors make more concrete decisions (Tumasjan et al., 2010). Furthermore, the major obstacle to working on social media datasets in sentiment analysis is the need for available labeled instances to build supervised learning models (Aroyehun and Gelbukh, 2018). ...
... Technically, the authors employed a lexicon-based approach, where they manually assigned a sentiment score to each word in the tweets and computed an overall sentiment score to identify the final sentiment category. Another study conducted by Tumasjan et al. (2010) utilized a similar approach to analyze the sentiment of tweets about political parties during the German federal election. The authors found that the sentiment of tweets about a particular party was positively correlated with the election results for that party, while the sentiment of tweets about the overall election was positively correlated with the election results (Tun and Khaing, 2023). ...
One of the widely studied text classification efforts is sentiment analysis. It is a specific examination involving natural language processing and machine learning methods to understand semantic orientation from textual data. Working social media posts, such as tweets, for sentiment analysis, is quite common among researchers due to the speed of information dissemination. In this regard, forecasting stock market tweets is a widely studied research topic. Some studies have revealed a strong connection between sentiment and stock market performance, while others have not found any notable associations. The proposed work shows two distinct approaches to sentiment analysis over the stock market tweets. The first approach employs traditional machine learning algorithms, including logistic regression, random forest, and XGBoost. The second approach constructs deep learning (as a subfield of machine learning) models using LSTM and CNN algorithms to classify the test instances into positive, negative, or neutral classes through ten randomly shuffled data splits. In this study, the labeled data size is gradually increased utilizing a pre-trained model, FinBERT. It is exclusively employed to label unlabeled data instances to integrate them into the experiments. The goal is to monitor the effect of the additional newly-labeled examples on the sentiment analysis performance. The experiments showed that the average F1-score improved by 20% for the deep learning models and 17% for the machine learning models. In the end, the paper reveals a strong positive correlation between training data size and the classification performance of the experimental approaches.
... Advanced NLP techniques, such as sentiment lexicons, and ML algorithms, including supervised and unsupervised learning models, are deployed to classify the sentiment of each article. This approach facilitates a granular analysis of sentiment distribution across different news outlets and an exploration of the interplay between article sentiment and factors, such as topic, publication date, and source [17,18]. ...
... • Public opinion and social impact: Studies have connected news content sentiment to shifts in public opinion, suggesting that news media sentiment can influence societal attitudes and behaviors [15]. For instance, Twitter sentiment analysis has shown correlations between news sentiment on this platform and political election outcomes [18]. • Financial markets: Sentiment analysis of news media has been applied to forecast market movements and investor behavior in the financial sector, with studies using BERT and other NLP models demonstrating the potential of news sentiment analysis in predicting stock market trends [17]. ...
The study examines the opinions expressed in 143,000 reports from 15 different U.S. news sources between 2000 and 2017 using cutting-edge Natural Language Processing (NLP) approaches, such as Recurrent Neural Networks with Long Short-Term Memory units and Transformer-based models like BERT and GPT. Unlike previous studies that focus on short-term sentiment analysis or limited sources, our comprehensive dataset spans nearly two decades and encompasses a diverse range of publications, capturing evolving emotions and media biases over time. Web scraping and intensive preprocessing were used to carefully curate the dataset, which captured the changing emotions over time across a variety of publications. The sentiment analysis shows that media coverage is generally biased in a positive way, with notable variations that correspond to important world events. Additionally, the study reveals significant emotional variation among news organizations, which reflects their distinct editorial stances and target audiences. The emotional tone of articles is also clearly influenced by the individual authors, highlighting the part that individual writing styles have in influencing public opinion. Furthermore, linguistic diversity and sentiment expression are found to be correlated, indicating that more complex emotional tones may be linked to a diverse vocabulary. Consistent changes in emotion over time are shown by the temporal analysis, which corresponds with sociopolitical and economic advancements. These results show how important media are in reflecting public sentiments and how well NLP methods work to glean insightful information from massive amounts of textual data.
... • Development of more advanced technology [17,18] • Development of clear ethical and regulatory frameworks [19,20] • Integration with other technologies such as IoT, big data, and AI [19,21] • Enhancing digital literacy to understand the potential and risks of this technology [20,22] ...
... The development of more advanced sentiment analysis technology presents both challenges and opportunities. Ongoing research aims to improve the accuracy and efficiency of the algorithms used [17,18]. Moreover, developing clear ethical and regulatory frameworks is essential to ensure the responsible use of this technology [19,20]. ...
... Moreover, sentiment analysis has found applications beyond social media. In e-commerce, sentiment analysis is used to analyze customer reviews and feedback, while in healthcare, it is used to improve patient care and service delivery [9,10]. In politics, sentiment analysis is used to analyze public opinion and sentiment towards political candidates and issues, providing valuable insights for political campaigns and decision-making [10]. ...
... In e-commerce, sentiment analysis is used to analyze customer reviews and feedback, while in healthcare, it is used to improve patient care and service delivery [9,10]. In politics, sentiment analysis is used to analyze public opinion and sentiment towards political candidates and issues, providing valuable insights for political campaigns and decision-making [10]. The author [11] made a significant contribution to sentiment analysis by introducing semantic sentiment analysis of Twitter data. ...
In today's digital era, understanding the sentiment of the public towards online content is crucial for
various purposes like marketing, public opinion analysis, and content moderation. This research introduces a new
method for analyzing sentiment in YouTube comments using natural language processing (NLP) techniques. The
technique uses the Natural Language Toolkit (NLTK) Python package to do sentiment analysis after gathering
comments from a particular movie using the YouTube API. In order to analyse sentiment in YouTube comments,
this research study presents a useful and effective approach that advances the field of sentiment analysis. The
proposed approach can be extended to analyze sentiment in other social media platforms and can be integrated into
applications for real-time sentiment monitoring and analysis
... In addition to following each other, there are other ways in which users can publicly interact such as re-tweeting (passing forward another user's tweet), and mentioning each other in tweets. Twitter has been a popular venue for the dissemination of information, memes, opinions, and has facilitated public debate about a variety of subjects [2,4,5,18,21,25,35]. As a result, Twitter has received considerable attention from researchers who wish to gain insights into the relationships and mechanisms that govern these social interactions [11]. ...
... The use of sentiment analysis to infer the disposition of individuals or groups towards specific topics is a growing area of interest in computational social science [7,11,14,23,26,32]. For example, sentiment analysis on Twitter data has been used to study stock market fluctuations [8,38], film box-office performance [3] and reviews [36], tracking the spread of influenza [22], and (albeit controversially) predicting elections [6,24,29,35,37]. Although some of these studies have well-noted shortcomings [16,17], the idea of using the content of tweets to gain insight into social phenomena remains a promising and compelling one. ...
We examine the relationship between social structure and sentiment through the analysis of a large collection of tweets about the Irish Marriage Referendum of 2015. We obtain the sentiment of every tweet with the hashtags #marref and #marriageref that was posted in the days leading to the referendum, and construct networks to aggregate sentiment and use it to study the interactions among users. Our results show that the sentiment of mention tweets posted by users is correlated with the sentiment of received mentions, and there are significantly more connections between users with similar sentiment scores than among users with opposite scores in the mention and follower networks. We combine the community structure of the two networks with the activity level of the users and sentiment scores to find groups of users who support voting `yes' or `no' in the referendum. There were numerous conversations between users on opposing sides of the debate in the absence of follower connections, which suggests that there were efforts by some users to establish dialogue and debate across ideological divisions. Our analysis shows that social structure can be integrated successfully with sentiment to analyse and understand the disposition of social media users. These results have potential applications in the integration of data and meta-data to study opinion dynamics, public opinion modelling, and polling.
... There has been a gamut of research which links internet search behaviour to ground truths such as symptoms of illness, political election, or major sporting events [14], [15], [16], [17], [18], [19]. Detection of utility change in online search, therefore, is helpful to identify changes in ground truth and useful, for example, for early containment of diseases [15] or predicting changes in political opinion [20], [21]. Also, the intrinsic nature of the online search utility function motivates such a study under a revealed preference setting. ...
... However, from an analytical expression for the lower bound on false alarm probability, we can obtain an upper bound of the test statistic, denoted by Φ * (y). Hence, for any dataset D obs in (17), if the solution to the optimization problem (20) is such that Φ > Φ * (y), then the dataset does not satisfy utility maximization, for the desired false alarm probability. ...
This paper deals with change detection of utility maximization behaviour in online social media. Such changes occur due to the effect of marketing, advertising, or changes in ground truth. First, we use the revealed preference framework to detect the unknown time point (change point) at which the utility function changed. We derive necessary and sufficient conditions for detecting the change point. Second, in the presence of noisy measurements, we propose a method to detect the change point and construct a decision test. Also, an optimization criteria is provided to recover the linear perturbation coefficients. Finally, to reduce the computational cost, a dimensionality reduction algorithm using Johnson-Lindenstrauss transform is presented. The results developed are illustrated on two real datasets: Yahoo! Tech Buzz dataset and Youstatanalyzer dataset. By using the results developed in the paper, several useful insights can be gleaned from these data sets. First, the changes in ground truth affecting the utility of the agent can be detected by utility maximization behaviour in online search. Second, the recovered utility functions satisfy the single crossing property indicating strategic substitute behaviour in online search. Third, due to the large number of videos in YouTube, the utility maximization behaviour was verified through the dimensionality reduction algorithm. Finally, using the utility function recovered in the lower dimension, we devise an algorithm to predict total traffic in YouTube.
... For our specific objectives, studies show that Twitter users are responsive to political happenings (Wang et al. 2012) and engage with and share information that's favorable to their own political party (Shin and Thorson 2017). Additional work finds that quantity and content of political tweets can predict public opinion (Davis et al. 2017;Tumasjan et al. 2010;O'Connor et al. 2010) and offer valuable information for decision-making, candidate popularity, forecasting, and governance and public trust (Tumasjan et al. 2010;Yaqub et al. 2017). In many aspects, the statements made on Twitter align with the conceptual framework of Zaller's theory of public opinion formation (Zaller 1992). ...
... For our specific objectives, studies show that Twitter users are responsive to political happenings (Wang et al. 2012) and engage with and share information that's favorable to their own political party (Shin and Thorson 2017). Additional work finds that quantity and content of political tweets can predict public opinion (Davis et al. 2017;Tumasjan et al. 2010;O'Connor et al. 2010) and offer valuable information for decision-making, candidate popularity, forecasting, and governance and public trust (Tumasjan et al. 2010;Yaqub et al. 2017). In many aspects, the statements made on Twitter align with the conceptual framework of Zaller's theory of public opinion formation (Zaller 1992). ...
We analyze a cache of tweets from partisan users concerning the confirmation hearings of Justices Brett Kavanaugh, Amy Coney Barrett, and Ketanji Brown Jackson. Using these original data, we investigate how Twitter users with partisan leanings interact with judicial nominations and confirmations. We find that these users tend to exhibit behavior consistent with offline partisan dynamics. Our analysis reveals that Democrats and Republicans express distinct emotional responses based on the alignment of nominees with their respective parties. Additionally, our study highlights the active participation of partisans in promoting politically charged topics throughout the confirmation process, starting from the vacancy stage.
... There are several studies on this topic in the cultural industry, such as measuring the success of a film by simply weighing its mentions [42]. Regarding the relationship between social media commitment and votes, several studies show a directly proportional relationship between commitment and number of votes cast [43], or also a direct correlation between the result and the mentions of parties by users [44]. There are even some studies on Congressional election cycles in the United States where there is a statistically relevant relationship between tweets and results. ...
... Surveys and opinion polls have been used to measure Public Opinion, even if Twitter can be understood as a ''poll'' that conducts a more systematic measurement, although it is less structured due to the amount of noise and metalanguages on this social network [70]. Through the socalled opinion mining, we can extract a political sentiment [44] that does not necessarily match the levels of political support, reason for which it is not always considered a good opinion ''meter'' [71]. The measurement method is under discussion and there are many ways of making it operational through significant connections, visualising key actors and climates of opinion that were previously invisible [72]. ...
La sociedad digital ha propiciado cambios de carácter instrumental y una actualización de la epistemología que ayuda a comprender un nuevo espacio público en dónde interactúan partidos políticos, instituciones, medios de comunicación y ciudadanía. Esta tesis doctoral por compendio de publicaciones estudia los mensajes en Twitter de diecinueve candidatos, de diez televisiones y de más de medio millón de usuarios en diez elecciones presidenciales latinoamericanas celebradas entre los años 2015 y 2019, con el objetivo de analizar el proceso de construcción de las agendas políticas digitales. A través de la técnica del análisis de contenido y del análisis de redes sociales (social network analysis), se han introducido variables como la frecuencia de actividad, temas, el tipo de usos políticos de la red social, la polarización y las emociones para explicar el comportamiento de actores políticos y comunidades digitales. Entre los resultados más relevantes destacan principalmente cinco: en primer lugar, el uso de Twitter por parte de los candidatos latinoamericanos sigue un modelo de marketing político poco profesionalizado, intuitivo y que no tiene en cuenta las potencialidades de la red social; en segundo término, las democracias latinoamericanas con un carácter más consolidado tienden a generar agendas de temas más cohesionadas, independientemente de la ideología del candidato; posteriormente, el modelo de paralelismo también se expande al espacio digital, reforzando la coalición temática entre medios y candidatos con un cierto control político manifestado a través de mecanismos formales que pautan el ejercicio de producción de la información (encuadres), en el que las comunidades son las que realizan una exposición selectiva; en cuarto lugar, se constata un modelo de comportamiento de los usuarios con una generación acompasada de emociones entre todas las comunidades estatales (contagio emocional), lo que en cierta medida rompe la idea de cámara de eco totalmente estanca; finalmente, se presenta una polarización intrínseca a las características de la red y no preconstruida. Si bien existen ciertos impulsos en cuanto al aumento y disminución de la polarización, esta no se sostiene y se acaba autorregulando. Esto genera una nueva defensa de la tesis del modelo innato de la polarización, volviendo a reabrir el debate sobre la “caja negra” de las redes sociales, que en alguna literatura ya parecía cerrada.
... Research has shown that social media analysis, especially of political conversations, can uncover sentiment shifts and public opinion trends (Bodo, 2022). These insights are crucial for understanding voter behavior and predicting election outcomes, as evidenced by studies analyzing Twitter data during the 2016 U.S. election (Tumasjan et al., 2010). ...
In today's digital era, social media data provides valuable insights into public opinion. This study implements the Graph Regularized Probabilistic Latent Semantic Analysis (GPLSA) method to analyze topics from social media data surrounding the 2024 Indonesian Presidential Election (Pemilu), as well as to evaluate the efficiency of the Probabilistic Latent Semantic Analysis (PLSA) algorithm. The research stages include collecting social media data on presidential debates and elections, text pre-processing, and applying the GPLSA method to identify main topics. The analysis results show that PLSA without graph achieved a topic coherence score of 0.653, indicating good consistency, while GPLSA decreased to 0.5, suggesting that the addition of graph regularization did not significantly enhance coherence. Additionally, PLSA without graph achieved a perplexity score of 12.138, indicating good predictive capability, while GPLSA increased to 12.511, showing that graph regularization did not improve the prediction of new words. PLSA without graph also produced topics relevant to election issues, while GPLSA generated topics influenced by graph regularization, though without significant improvement in topic quality. Sentiment analysis of social media posts provides insights into public responses to debates and election issues. Validation of the GPLSA model ensures relevant topic representation. This research contributes to the development of text analysis methods and offers valuable information for elections and democratic participation. These results can be utilized by stakeholders to make more strategic and informed decisions.
... Using the lexicon-based sentiment analysis approach, positive, negative, or neutral sentiment in each upload is analyzed. This approach has proven effective in social mediarelated research, such as those used by Tumasjan et al (Tumasjan et al., 2010), which show that sentiment analysis can predict public responses to political candidates during campaign periods. In this way, the study will evaluate how the evolving narrative triggers an emotional reaction from social media users. ...
This study aims to analyze the pattern of issue formation and narratives that developed on social media during the implementation of the West Sumatra (West Sumatra) Regional Head Election (Pilkada) in 2024. The main focus of this research is to identify how key issues are raised and processed in conversations on social media and to understand the role of key actors in shaping political narratives. Content analysis methods and social networks are used to study patterns of interaction and information distribution on various social media platforms. The results show that certain issues are more prominent in influencing public opinion and get high user engagement, especially on themes related to local identity, regional policies, and political party dynamics. This finding is expected to contribute to understanding political dynamics in the digital era and the impact of social media on the formation of public opinion during the Regional Election process. In addition, the results of this research can be a reference for the government, political practitioners, and the public in managing political issues and narratives that develop on social media.
... In psychology research, many have used LIWC to detect depression [6,30] and to study how the language use of couples reltes to their marital satisfaction and the overall heath of the marriage [8,9]. LIWC has also been used to analyze text with political content to assess the public's opinion on political topics and to find common linguistic patterns in speech of famous politicians [31,32,33]. ...
Context: A deeper understanding of human factors in software engineering (SE) is essential for improving team collaboration, decision-making, and productivity. Communication channels like code reviews and chats provide insights into developers' psychological and emotional states. While large language models excel at text analysis, they often lack transparency and precision. Psycholinguistic tools like Linguistic Inquiry and Word Count (LIWC) offer clearer, interpretable insights into cognitive and emotional processes exhibited in text. Despite its wide use in SE research, no comprehensive review of LIWC's use has been conducted. Objective: We examine the importance of psycholinguistic tools, particularly LIWC, and provide a thorough analysis of its current and potential future applications in SE research. Methods: We conducted a systematic review of six prominent databases, identifying 43 SE-related papers using LIWC. Our analysis focuses on five research questions. Results: Our findings reveal a wide range of applications, including analyzing team communication to detect developer emotions and personality, developing ML models to predict deleted Stack Overflow posts, and more recently comparing AI-generated and human-written text. LIWC has been primarily used with data from project management platforms (e.g., GitHub) and Q&A forums (e.g., Stack Overflow). Key BSE concepts include Communication, Organizational Climate, and Positive Psychology. 26 of 43 papers did not formally evaluate LIWC. Concerns were raised about some limitations, including difficulty handling SE-specific vocabulary. Conclusion: We highlight the potential of psycholinguistic tools and their limitations, and present new use cases for advancing the research of human factors in SE (e.g., bias in human-LLM conversations).
... AI-driven market intelligence provides businesses with real-time analytics and predictive modeling that enable them to identify emerging trends, analyze shifts in consumer sentiment, enhance demand forecasting, automate competitive analysis, and improve marketing strategies [12], [13], [14]. By analyzing social media conversations [15], online reviews [16], and digital forums [17], AI models can detect early signs of consumer interest in specific products or services. Similarly, sentiment analysis applied to large-scale consumer interactions helps businesses understand public perception toward brands and industries [18], [19]. ...
In the era of digital transformation, businesses must leverage Artificial Intelligence (AI)-driven market intelligence to predict consumer trends and optimize strategic decision-making. This research presents an AI-augmented predictive analytics framework that integrates Machine Learning (ML) and Natural Language Processing (NLP) to extract actionable insights from diverse unstructured data sources, including social media (e.g., Twitter, Instagram), product reviews (e.g., Amazon, Yelp), and news articles. By leveraging Transformerbased NLP models (e.g., BERT, RoBERTa), sentiment analysis, and time-series forecasting techniques (e.g., ARIMA, LSTMs), the framework identifies emerging consumer preferences, shifts in brand perception, and evolving market demand patterns. The study rigorously benchmarks various ML and deep learning models against baseline approaches, demonstrating a 20% improvement in trend prediction accuracy and up to 60% reduction in decision-making latency. Additionally, we explore the scalability of AI models for real-time trend monitoring, discuss the ethical implications of consumer data analysis, and outline how AIdriven insights empower businesses to enhance product innovation, marketing strategies, and competitive positioning. This research underscores the transformative potential of AI in market intelligence, providing organizations with a datadriven approach to dynamically adapt to evolving consumer behavior.
... A long standing goal of AI question-answering systems has been to provide multiple perspectives to controversial queries [3,6,7,14,27]. Applications aimed at summarizing diverse opinions range from helping people make more informed choices on product purchases [11] to predicting elections [30]. With the development of chatbots and LLMs playing an increasingly significant role in search and question answering, several authors such as Metzler et al. [17] have argued that there is growing importance to provide diverse, unbiased points of view. ...
This paper describes the construction of a dataset and the evaluation of training methods to improve generative large language models' (LLMs) ability to answer queries on sensitive topics with a Neutral Point of View (NPOV), i.e., to provide significantly more informative, diverse and impartial answers. The dataset, the SHQ-NPOV dataset, comprises 300 high-quality, human-written quadruplets: a query on a sensitive topic, an answer, an NPOV rating, and a set of links to source texts elaborating the various points of view. The first key contribution of this paper is a new methodology to create such datasets through iterative rounds of human peer-critique and annotator training, which we release alongside the dataset. The second key contribution is the identification of a highly effective training regime for parameter-efficient reinforcement learning (PE-RL) to improve NPOV generation. We compare and extensively evaluate PE-RL and multiple baselines-including LoRA finetuning (a strong baseline), SFT and RLHF. PE-RL not only improves on overall NPOV quality compared to the strongest baseline (), but also scores much higher on features linguists identify as key to separating good answers from the best answers ( for presence of supportive details, for absence of oversimplification). A qualitative analysis corroborates this. Finally, our evaluation finds no statistical differences between results on topics that appear in the training dataset and those on separated evaluation topics, which provides strong evidence that our approach to training PE-RL exhibits very effective out of topic generalization.
... Research work shows that the existing multi-label emotion corpus is developed to analyze user tweets, US elections, microblogs, essays, and sentences. The research works [10,21,55] explore political emotions classification but are limited to single-label analysis. The related work for user emotions detection is further summarized in Table 1, which state that user emotions analysis is studied for both single-label and multi-label analysis with different datasets. ...
Understanding user emotions to identify user opinion, sentiment, stance, and preferences has become a hot topic of research in the last few years. Many studies and datasets are designed for user emotion analysis including news websites, blogs, and user tweets. However, there is little exploration of political emotions in the Indian context for multi-label emotion detection. This paper presents a PoliEMO dataset—a novel benchmark corpus of political tweets in a multi-label setup for Indian elections, consisting of over 3512 tweets manually annotated. In this work, 6792 labels were generated for six emotion categories: anger, insult, joy, neutral, sadness, and shameful. Next, PoliEMO dataset is used to understand emotions in a multi-label context using state-of-the-art machine learning algorithms with multi-label classifier (binary relevance (BR), label powerset (LP), classifier chain (CC), and multi-label k-nearest neighbors (MkNN)) and deep learning models like convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and transfer learning model, i.e., bidirectional encoder representations from transformers (BERT). Experiments and results show Bi-LSTM performs better with micro-averaged F1 score of 0.81, macro-averaged F1 score of 0.78, and accuracy 0.68 as compared to state-of-the-art approaches.
... Information Dissemination on Social Media:Social media platforms play a crucial role in shaping political discourse by acting as conduits for information dissemination. The ease and speed of sharing content on platforms such as Twitter (Barbera, 2015), Facebook, and Instagram make them powerful tools for political actors to reach and influence a broad audience(Tumasjan, 2010). Studies have shown that social media allows candidates to reach a broader audience in real-time, enabling them to share their policy positions, campaign messages, and responses to current events (Capella, 2003) (Jonathan Bright, 2019). ...
The advent of social media has revolutionized the landscape of political campaigning, reshaping traditional communication channels and strategies. This paper delves into the intricate interplay between social media and political campaigns, elucidating their profound impact on modern political discourse and voter engagement. It is all about the commencement by outlining the transformative role of social media in amplifying political messaging and expanding the reach of candidates. Social media is becoming a tool that examines the democratization of political participation through platforms that foster unprecedented levels of direct interaction between candidates and voters, thus promoting greater transparency and accessibility. This paper underscores the paramount importance of understanding the intricate interplay between social media and political campaigning. It emphasizes the potential of social media as a tool for fostering civic engagement and political empowerment, while cautioning against its pitfalls. By shedding light on the complexities inherent in this digital paradigm shift, this study contributes to a nuanced comprehension of how social media has irrevocably transformed the landscape of political campaigning, shaping the dynamics of democracy in the 21st century. It also considers the evolving ethical and regulatory landscape surrounding social media in political campaign, addressing concerns related to privacy, security and the potential for foreign interference.
... The use of affective-negative language is also evident in other parts of the world with advanced democracies. In the United States, political language has been shown to frequently be affective in traditional media, such as the New York Times (Young & Soroka 2011), to political blogs and Twitter (Vatrapu, Scott & Wimal, 2009;Tumasjan, Sprenger, Sandner & Welpe, 2010). ...
... Twitter data has been used for various tasks, e.g., event detection [2], sentiment analysis [3], breaking news analysis [4], rumor detection [5], community detection [6], election results prediction [7], and crime prediction [8]. ...
Event detection in a multimodal Twitter dataset is considered. We treat the hashtags in the dataset as instances with two modes: text and geolocation features. The text feature consists of a bag-of-words representation. The geolocation feature consists of geotags (i.e., geographical coordinates) of the tweets. Fusing the multimodal data we aim to detect, in terms of topic and geolocation, the interesting events and the associated hashtags. To this end, a generative latent variable model is assumed, and a generalized expectation-maximization (EM) algorithm is derived to learn the model parameters. The proposed method is computationally efficient, and lends itself to big datasets. Experimental results on a Twitter dataset from August 2014 show the efficacy of the proposed method.
... If political ideology plays an important role in influencing voting behavior, as it has been previously showed in other countries [20,21,7,6], we might expect that in the case of Colombian regional elections the linguistic similarities between politicians of the same ideological affiliation should be higher than the similarities they share with politicians of other ideologies. Alternatively, given the fact that politicians can exert their influence by increasing their Twitter activity in terms of the number of followers, the number of tweets and re-tweeted messages [22,23,24], we also might expect a statistical significant association between these metrics and their electoral results. ...
Propagation of political ideologies in social networks has shown a notorious impact on voting behavior. Both the contents of the messages (the ideology) and the politicians' influence on their online audiences (their followers) have been associated with such an impact. Here we evaluate which of these factors exerted a major role in deciding electoral results of the 2015 Colombian regional elections by evaluating the linguistic similarity of political ideologies and their influence on the Twitter sphere. The electoral results proved to be strongly associated with tweets and retweets and not with the linguistic content of their ideologies or their Twitter followers. Suggestions on new ways to analyze electoral processes are finally discussed.
... We use flu now-casting as one of the test cases in our experiments. One of the most popular applications of social media now-casting is public opinion monitoring and election prediction [33,41]. However, early claims of success in this area have come under scrutiny and the feasibility of such an endeavor seems uncertain [15,28,9]. ...
Given the ever increasing amount of publicly available social media data, there is growing interest in using online data to study and quantify phenomena in the offline "real" world. As social media data can be obtained in near real-time and at low cost, it is often used for "now-casting" indices such as levels of flu activity or unemployment. The term "social sensing" is often used in this context to describe the idea that users act as "sensors", publicly reporting their health status or job losses. Sensor activity during a time period is then typically aggregated in a "one tweet, one vote" fashion by simply counting. At the same time, researchers readily admit that social media users are not a perfect representation of the actual population. Additionally, users differ in the amount of details of their personal lives that they reveal. Intuitively, it should be possible to improve now-casting by assigning different weights to different user groups. In this paper, we ask "How does social sensing actually work?" or, more precisely, "Whom should we sense--and whom not--for optimal results?". We investigate how different sampling strategies affect the performance of now-casting of two common offline indices: flu activity and unemployment rate. We show that now-casting can be improved by 1) applying user filtering techniques and 2) selecting users with complete profiles. We also find that, using the right type of user groups, now-casting performance does not degrade, even when drastically reducing the size of the dataset. More fundamentally, we describe which type of users contribute most to the accuracy by asking if "babblers are better". We conclude the paper by providing guidance on how to select better user groups for more accurate now-casting.
... Social media have also been used for the study of political events, such as for elections (Williams & Gulati 2007, Tumasjan et al. 2010, Williams & Girish 2012, Bond et al. 2012) and protests (González-Bailón et al. 2011, 2013, Margetts et al. 2015. Through the analysis of social media it is possible to model the dynamics of election campaigns and map the public opinion of cities, regions and states. ...
In the past years we have witnessed the emergence of the new discipline of computational social science, which promotes a new data-driven and computation-based approach to social sciences. In this article we discuss how the availability of new technologies such as online social media and mobile smartphones has allowed researchers to passively collect human behavioral data at a scale and a level of granularity that were just unthinkable some years ago. We also discuss how these digital traces can then be used to prove (or disprove) existing theories and develop new models of human behavior.
... Among these, Twitter, a micro-blogging website that allows its users to express themselves in 140 characters, has emerged as a go-to source for current affairs, entertainment news and to seek information about global events in real-time. For example, Twitter has been used to study public reaction to events such as natural disasters [1], political elections [2] and terrorist attacks [3]. The England versus Iceland football match at the European Football Championships (Euro 2016) was one of the most tweeted about events of 2016attracting 2.1 million users [4]. ...
The popularity of Twitter for information discovery, coupled with the automatic shortening of URLs to save space, given the 140 character limit, provides cyber criminals with an opportunity to obfuscate the URL of a malicious Web page within a tweet. Once the URL is obfuscated the cyber criminal can lure a user to click on it with enticing text and images before carrying out a cyber attack using a malicious Web server. This is known as a drive-by- download. In a drive-by-download a user's computer system is infected while interacting with the malicious endpoint, often without them being made aware, the attack has taken place. An attacker can gain control of the system by exploiting unpatched system vulnerabilities and this form of attack currently represents one of the most common methods employed. In this paper, we build a machine learning model using machine activity data and tweet meta data to move beyond post-execution classification of such URLs as malicious, to predict a URL will be malicious with 99.2% F-measure (using 10-fold cross validation) and 83.98% (using an unseen test set) at 1 second into the interaction with the URL. Thus providing a basis from which to kill the connection to the server before an attack has completed and proactively blocking and preventing an attack, rather than reacting and repairing at a later date.
... More specifically, using data from Twitter, researchers have modeled the epidemics of colds in New York City [9] and post-partum mood changes among new mothers [3]. Similarly, these systems have been used to model daily lifestyles [5], urban dynamics [4], elections [14], and even disasters [2]. ...
Social media has become globally ubiquitous, transforming how people are networked and mobilized. This forum explores research and applications of these new networked publics at individual, organizational, and societal levels.
... Among computational methods to analyze tweets, computational linguistics is a well-known developed approach to gain insight into a population, track health issues, and discover new knowledge Dredze, 2011, 2012;Harris et al., 2014;Zhao et al., 2011). Twitter data has been used for a wide range of health and non-health related applications, such as stock market (Bollen et al., 2011) and election analysis (Tumasjan et al., 2010). Some examples of Twitter data analysis for health-related topics include: flu (Ritterman et al., 2009;Szomszor et al., 2010;Cristianini, 2012, 2010;Culotta, 2010), mental health (Coppersmith et al., 2015), Ebola (Lazard et al., 2015;Odlum and Yoon, 2015), Zika (Fu et al., 2016), medication use (Scanfeld et al., 2010;Hanson et al., 2013;Buntain and Golbeck, 2015), diabetes (Harris et al., 2013), and weight loss and obesity (Dahl et al., 2016;Ghosh and Guha, 2013;Vickey et al., 2013;Turner-McGrievy and Beets, 2015;Harris et al., 2014). ...
Social media provide a platform for users to express their opinions and share information. Understanding public health opinions on social media, such as Twitter, offers a unique approach to characterizing common health issues such as diabetes, diet, exercise, and obesity (DDEO), however, collecting and analyzing a large scale conversational public health data set is a challenging research task. The goal of this research is to analyze the characteristics of the general public's opinions in regard to diabetes, diet, exercise and obesity (DDEO) as expressed on Twitter. A multi-component semantic and linguistic framework was developed to collect Twitter data, discover topics of interest about DDEO, and analyze the topics. From the extracted 4.5 million tweets, 8% of tweets discussed diabetes, 23.7% diet, 16.6% exercise, and 51.7% obesity. The strongest correlation among the topics was determined between exercise and obesity. Other notable correlations were: diabetes and obesity, and diet and obesity DDEO terms were also identified as subtopics of each of the DDEO topics. The frequent subtopics discussed along with Diabetes, excluding the DDEO terms themselves, were blood pressure, heart attack, yoga, and Alzheimer. The non-DDEO subtopics for Diet included vegetarian, pregnancy, celebrities, weight loss, religious, and mental health, while subtopics for Exercise included computer games, brain, fitness, and daily plan. Non-DDEO subtopics for Obesity included Alzheimer, cancer, and children. With 2.67 billion social media users in 2016, publicly available data such as Twitter posts can be utilized to support clinical providers, public health experts, and social scientists in better understanding common public opinions in regard to diabetes, diet, exercise, and obesity.
... We are interested in how many users have political parties' information delivered to them. Tumasjan et al. reported that Twitter is functioning as a discussion forum for politics [13], and, there have been many studies on users' access to political information on social media. Previous studies show that users are divided regarding access to political information [1,8,9,10]. ...
In modern election campaigns, political parties utilize social media to advertise their policies and candidates and to communicate to electorates. In Japan's latest general election in 2017, the 48th general election for the Lower House, social media, especially Twitter, was actively used. In this paper, we perform a detailed analysis of social graphs and users who retweeted tweets of political parties during the election. Our aim is to obtain accurate information regarding the diffusion power for each party rather than just the number of followers. The results indicate that a user following a user who follows a political party account tended to also follow the account. This means that it does not increase diversity because users who follow each other tend to share similar values. We also find that followers of a specific party frequently retweeted the tweets. However, since users following the user who follow a political party account are not diverse, political parties delivered the information only to a few political detachment users.
... Different studies used the frequency of tweets to determine the popularity of candidates (Tumasjan et al., 2010;Gaurav et al., 2013;Boutet et al., 2012). A similar method was developed based on the frequency of tweets that mentioned names of political parties, political candidates, and contested constituencies to predict the 2011 Singapore general election (Skoric et al., 2012). ...
Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This paper proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election.
... There is a burgeoning literature in computer science on using social media data to analyze and predict elections. Research by (Tumasjan et al. 2010) finds that the number of messages mentioning a party reflects the election results. According to (Williams and Gulati 2008), the number of Facebook fans constitutes an indicator of candidate viability. ...
In this paper, we analyze the growth patterns of Donald Trump's followers (Trumpists, henceforth) on Twitter. We first construct a random walk model with a time trend to study the growth trend and the effects of public debates. We then analyze the relationship between Trump's activity on Twitter and the growth of his followers. Thirdly, we analyze the effects of such controversial events as calling for Muslim ban and his 'schlonged' remark.
... As a whole, mobile clients for microblogging platforms, social networking tools, and other "proxy" data of human activity collected in the web allow for the quantitative analysis of social systems at a scale that would have been unimaginable just a few years ago [3][4][5][6]. In particular, the possibility of using mobileenabled microblogging platforms, such as Twitter, as monitors of public opinion, social movements and as tools for the mapping of social communities has generated much interest in the literature [7][8][9][10][11][12][13][14]. At the same time it is crucial to understand to which extent the picture of socio-technical systems emerging from digital data proxies is a statistically sound and how well it does scale to a planetary dimension [15]. ...
Large scale analysis and statistics of socio-technical systems that just a few short years ago would have required the use of consistent economic and human resources can nowadays be conveniently performed by mining the enormous amount of digital data produced by human activities. Although a characterization of several aspects of our societies is emerging from the data revolution, a number of questions concerning the reliability and the biases inherent to the big data "proxies" of social life are still open. Here, we survey worldwide linguistic indicators and trends through the analysis of a large-scale dataset of microblogging posts. We show that available data allow for the study of language geography at scales ranging from country-level aggregation to specific city neighborhoods. The high resolution and coverage of the data allows us to investigate different indicators such as the linguistic homogeneity of different countries, the touristic seasonal patterns within countries and the geographical distribution of different languages in multilingual regions. This work highlights the potential of geolocalized studies of open data sources to improve current analysis and develop indicators for major social phenomena in specific communities.
... There are several studies that retrieve information from social media to analyze and predict real-world phenomena such as stock market [3], migration [24], election [31,40], and also political leaning of Twitter users [19,33]. ...
Social media is considered a democratic space in which people connect and interact with each other regardless of their gender, race, or any other demographic factor. Despite numerous efforts that explore demographic factors in social media, it is still unclear whether social media perpetuates old inequalities from the offline world. In this paper, we attempt to identify gender and race of Twitter users located in U.S. using advanced image processing algorithms from Face++. Then, we investigate how different demographic groups (i.e. male/female, Asian/Black/White) connect with other. We quantify to what extent one group follow and interact with each other and the extent to which these connections and interactions reflect in inequalities in Twitter. Our analysis shows that users identified as White and male tend to attain higher positions in Twitter, in terms of the number of followers and number of times in user's lists. We hope our effort can stimulate the development of new theories of demographic information in the online space.
... Golbeck, Grimes and Rogers (2010) find by text mining tweets that members of the United States Congress employ Twitter for primarily two purposes: information dissemination and self promotion. Tumasjan et al. (2010) find that the number of tweets from the general public mentioning a political party or politician is a valid indicator of political sentiment and a good predictor of federal election results in Germany. More recently, similar results have been found for federal elections in Australia and the U.S. House of Representatives [Unankard et al. (2014), McKelvey, DiGrazia and Rojas (2014)]. ...
The rise of social media platforms has fundamentally altered the public discourse by providing easy to use and ubiquitous forums for the exchange of ideas and opinions. Elected officials often use such platforms for communication with the broader public to disseminate information and engage with their constituencies and other public officials. In this work, we investigate whether Twitter conversations between legislators reveal their real-world position and influence by analyzing multiple Twitter networks that feature different types of link relations between the Members of Parliament (MPs) in the United Kingdom and an identical data set for politicians within Ireland. We develop and apply a matrix factorization technique that allows the analyst to emphasize nodes with contextual local network structures by specifying network statistics that guide the factorization solution. Leveraging only link relation data, we find that important politicians in Twitter networks are associated with real-world leadership positions, and that rankings from the proposed method are correlated with the number of future media headlines.
... There are two main reasons to automate sentiment classification: first, the abundance of online materials, which is the result of web development, is beyond human analysis; and second, public opinion is a significant consideration when governments, institutions and individuals are making decisions and taking actions. Many diverse domains and applications can benefit from SA, including those in the political [49,19] linguistic [15] medical and social issues [36] and financial [35,43,42] domains. Thus a considerable attention has been drawn to SA and closely-related research directions such as emotion detection [27,4], subjectivity analysis [30], irony detection [39,38], and contention texts analysis [47]. ...
Products reviews are one of the major resources to determine the public sentiment. The existing literature on reviews sentiment analysis mainly utilizes supervised paradigm, which needs labeled data to be trained on and suffers from domain-dependency. This article addresses these issues by describes a completely automatic approach for sentiment analysis based on unsupervised ensemble learning. The method consists of two phases. The first phase is contextual analysis, which has five processes, namely (1) data preparation; (2) spelling correction; (3) intensifier handling; (4) negation handling and (5) contrast handling. The second phase comprises the unsupervised learning approach, which is an ensemble of clustering classifiers using a majority voting mechanism with different weight schemes. The base classifier of the ensemble method is a modified k-means algorithm. The base classifier is modified by extracting initial centroids from the feature set via using SentWordNet (SWN). We also introduce new sentiment analysis problems of Australian airlines and home builders which offer potential benchmark problems in the sentiment analysis field. Our experiments on datasets from different domains show that contextual analysis and the ensemble phases improve the clustering performance in term of accuracy, stability and generalization ability.
... Currently, researches on microblog sentiment analysis have been conducted on polarity classification ( Barbosa and Feng, 2010;Jiang el al., 2011;Speriosu et al., 2011) and have been proved to be useful in many applications, such as opinion polling (Tang et al., 2012), election prediction (Tumasjan et al., 2010) and even stock market prediction (Bollen et al., 2011). However, classifying microblog texts at the sentence level is often insufficient for applications because it does not identify the opinion targets. ...
... Researchers have used sentiment analysis to analyze tweets related to political candidates, revealing insights into voter sentiment and predicting election outcomes. For example, a study by Tumasjan et al. (2010) demonstrated that sentiment analysis of Twitter data could predict the results of the 2009 German federal election with remarkable accuracy, showcasing the potential of social media as a barometer for public sentiment. ...
Social media platforms contain vast amounts of data that can reveal public sentiment on various topics. This research explores the application of deep learning techniques, particularly convolutional neural networks (CNN) and recurrent neural networks (RNN), to analyze sentiment within social media text. The results indicate that these models achieve high accuracy in sentiment classification, making them valuable tools for companies seeking to understand public opinion.
... It is also employed in the political sphere to gauge public sentiment towards policies, debates, and election messages. This provides political parties and candidates with valuable insights into their campaign strategy (Tumasjan et al., 2010). ...
This article explores public perceptions on the Fourth Industrial Revolution (4IR) through an analysis of social media discourse across six European countries. Using sentiment analysis and machine learning techniques on a dataset of tweets and media articles, we assess how the public reacts to the integration of technologies such as artificial intelligence, robotics, and blockchain into society. The results highlight a significant polarization of opinions, with a shift from neutral to more definitive stances either embracing or resisting technological impacts. Positive sentiments are often associated with technological enhancements in quality of life and economic opportunities, whereas concerns focus on issues of privacy, data security, and ethical implications. This polarization underscores the need for policymakers to engage proactively with the public to address fears and harness the benefits of 4IR technologies. The findings also advocate for digital literacy and public awareness programs to mitigate misinformation and foster an informed public discourse on future technological integration. This study contributes to the ongoing debate on aligning technological advances with societal values and needs, emphasizing the role of informed public opinion in shaping effective policy.
... In this study With respect to [13], the mere number of messages reflects the election result and even comes close to traditional election polls. Using LIWC text analysis software, they conducted analysis of over 100,000 messages containing a reference to either a political party or a politician. ...
We are conducting sentiment analysis on Twitter with the power of huge language models, specifically GPT, to forecast election results. The widespread adoption of digital technology has resulted in a notable surge in the creation of user-generated content, which has therefore provoked a profound shift in the dynamics of communication across diverse platforms. Specifically, social media platforms have developed into veritable gold mines of behavioral data that offer profound insights into a range of industries, including politics, e-commerce, healthcare, and education. The application of predictive analytics to political tweet mining presents several difficulties, the most important of which are the precise evaluation of sentiment correctness and the identification of propagandistic storylines. We recommend employing LLMs as a solution because of their expertise in natural language processing (NLP) tasks, particularly GPT. LLMs have received extensive training, which has enabled them to understand context, sentiment, and other intricate linguistic subtleties. They are crucial to sentiment analysis because of their capacity to write coherent language. Our objective is to anticipate the outcomes of the Indian Lok Sabha Elections in 2024 by combining sentiment research with GPT models and taking advantage of these advantages. This study addresses the pressing need for reliable methodology in election outcome prediction by utilizing the strengths of LLMs and NLP approaches.
... As is generally known, big and rich data from social media such as blogs, Facebook and Twitter have turned the web into a user-generated repository of information in everincreasing numbers of areas. For instance, Twitter data have been used in social sciences to study the Arab spring (Campbell, 2011), to predict political campaigns (Gayo Avello et al., 2011;Tumasjan et al., 2010) and to predict stock markets (Bollen et al., 2011), and to model the geographic diffusion of new lexis (Eisenstein et al., 2014). Recent attempts also include incorporating data from various sources for applied purposes, such as the modelling of the impact of social networks in purchase intentions (Wang et al., 2016). ...
This article presents the Nordic Tweet Stream (NTS), a cross-disciplinary corpus project of computer scientists and a group of sociolinguists interested in language variability and in the global spread of English. Our research integrates two types of empirical data: We not only rely on traditional structured corpus data but also use unstructured data sources that are often big and rich in metadata, such as Twitter streams. The NTS downloads tweets and associated metadata from Denmark, Finland, Iceland, Norway and Sweden. We first introduce some technical aspects in creating a dynamic real-time monitor corpus, and the following case study illustrates how the corpus could be used as empirical evidence in sociolinguistic studies focusing on the global spread of English to multilingual settings. The results show that English is the most frequently used language, accounting for almost a third. These results can be used to assess how widespread English use is in the Nordic region and offer a big data perspective that complement previous small-scale studies. The future objectives include annotating the material, making it available for the scholarly community, and expanding the geographic scope of the data stream outside Nordic region.
... Because of the relatively easy access to tweets and their metadata, Twitter 4 has become a popular source of data for investigations of a number of phenomena. These include for instance studies of the Arab Spring [1], various political campaigns [2,3], of Twitter as a tool for emergency communication [4,5], and using social media data to predict stock market prices [6]. In linguistics, various mono- [7] and multilingual text corpora of tweets [8] have been built recently and used in a wide range of subfields (e.g. ...
This article describes our work in developing an application that recognizes automatically generated tweets. The objective of this machine learning application is to increase data accuracy in sociolinguistic studies that utilize Twitter by reducing skewed sampling and inaccuracies in linguistic data. Most previous machine learning attempts to exclude bot material have been language dependent since they make use of monolingual Twitter text in their training phase. In this paper, we present a language independent approach which classifies each single tweet to be either autogenerated (AGT) or human-generated (HGT). We define an AGT as a tweet where all or parts of the natural language content is generated automatically by a bot or other type of program. In other words, while AGT/HGT refer to an individual message, the term bot refers to non-personal and automated accounts that post content to online social networks. Our approach classifies a tweet using only metadata that comes with every tweet, and we utilize those metadata parameters that are both language and country independent. The empirical part shows good success rates. Using a bilingual training set of Finnish and Swedish tweets, we correctly classified about 98.2% of all tweets in a test set using a third language (English)
... • Politics: The stakeholders in political campaigns and analysis understood that Sentiment Analysis would help in monitoring the view of the public in political causes and candidates [5]. ...
This study examines the comparative performance of one-dimensional (1D) and two-dimensional (2D) Convolutional Neural Networks (CNNs) in processing sequential data for sentiment analysis, using Spotify music reviews as a case study. Leveraging a custom dataset from Kaggle, the study examines the effectiveness of CNN architectures in extracting meaningful patterns from text input. The study integrates PyTorch and TorchText for efficient data preprocessing and model deployment. Both architectures are evaluated based on classification accuracy, computational efficiency, and ability to handle sequential dependencies. The results highlight the strengths and limitations of each method, providing insight into their suitability for similar tasks in text-based sentiment analysis. This research provides valuable guidance for researchers and practitioners working on sequential data tasks, emphasizing the role of architectural design in achieving optimal performance.
This project focuses on building a sentiment analysis system to aid in election prediction by analysing tweets related to political leaders. It leverages machine learning, specifically the Passive Aggressive Classifier, and incorporates text preprocessing techniques such as TF-IDF vectorization to classify tweets as either positive or negative. A user-friendly Flask web application is developed, allowing users to upload CSV files containing tweets. The application processes the data and displays the sentiment distribution through a pie chart visualization. By capturing public sentiment on social media, the system offers valuable insights that can contribute to forecasting election outcomes. Keywords: Sentiment Analysis, Election Prediction, Tweet Classification, Passive Aggressive Classifier
The post-pandemic transition from remote work to office work has led to diverse opinions among employees, ranging from financial incentives to workplace comfort. This paper presents a comprehensive sentiment analysis and topic modeling approach applied to user-generated Reddit comments. Utilizing Natural Language Processing (NLP) techniques such as VADER Sentiment Analysis and Latent Dirichlet Allocation (LDA), the study classifies comments into sentiment categories and extracts prominent themes. The dataset, comprising 465 Reddit comments, underwent preprocessing, analysis, and visualization. Key findings indicate that salary increases (60%) are the most cited motivator for returning to office spaces, followed by workplace comfort (20%) and public transport improvements (20%). This research provides actionable insights for organizations to formulate data-driven policies and strategies for encouraging employees to return to office work.
The rapid rise of social media has fundamentally reshaped the way societies engage in political discourse, offering unprecedented opportunities and challenges. This paper delves deeply into the multifaceted role of social media platforms in shaping political discussions, exploring how they influence public engagement, polarization, and democratic processes. Through a comparative analysis of the United States, India, and Brazil, the paper examines the dual nature of social media as both a democratizing force and a disruptor of political harmony. By highlighting cultural, systemic, and platform-specific factors, the study underscores the complexities inherent in navigating social media's impact on political dynamics. Abstract: Social media platforms have revolutionized political discourse, providing spaces for diverse voices while simultaneously amplifying polarization and misinformation. This paper examines the interplay between social media and political dynamics, focusing on how platform design, cultural specificity, and political systems shape outcomes in different contexts. Using case studies from the United States, India, and Brazil, the analysis highlights the opportunities and challenges posed by social media, including its democratizing potential, the risks of echo chambers, and its influence on elections and social movements. The paper concludes with recommendations for multi-stakeholder approaches to address these challenges and enhance the role of social media in fostering informed and inclusive political participation.
Günümüzde sosyal medya önemli bir kitle iletişim aracı olarak toplumun birçok kesimi tarafından farklı amaçlar için kullanılmaktadır. Yerel yönetimler için de kurumsal sosyal medya hesapları önemli bir bilgilendirme ve etkileşim platformudur. Kurumsal sosyal medya hesaplarının yanında belediye başkanlarının kendilerine ait kişisel sosyal medya hesapları da bulunmaktadır. Belediyelerin resmi sosyal medya hesapları genellikle belediye ile ilgili çeşitli bilgilendirmeler yapmak için kullanılırken belediye başkanlarının hesapları hem belediye ile ilgili bilgilendirmeler hem kişisel bazı bilgilendirmeleri, görüşleri yansıtmaktadır. Bu çalışma, İzmir iline bağlı ilçe belediye başkanlarının X (eski adıyla Twitter) üzerindeki etkileşimlerini analiz ederek, sosyal medya stratejilerinin etkinliğini değerlendirmeyi amaçlamaktadır. Araştırma, belediye başkanlarının sosyal medya platformlarını nasıl kullandığını anlamak ve kamu ile iletişimlerini nasıl şekillendirdiklerini incelemeyi amaçlamaktadır. Belediye başkanlarının paylaşım içerikleri, paylaşım yapıları, zamanlamaları ve dönemsel eğilimler dikkate alınarak, beğeni ve tekrar paylaşım sayıları üzerinden etkileşim oranları incelenmiştir. Elde edilen bulgular, belediye başkanlarının sosyal medya platformlarını siyasi söylemler açısında etkin bir şekilde kullandığını göstermektedir. Ancak vatandaşla iletişim açısından tercih edilmediği görülmektedir. Araştırma bulguları, paylaşım uzunluklarının, etiket kullanımının, günün saatlerinin ve yılın farklı dönemlerinin etkileşim üzerinde önemli etkileri olduğunu göstermektedir. Yapılan paylaşımların hafta sonu, akşam ve gece saatlerinde fazla etkileşim aldığı görülmüştür. Bu bulgular, belediye başkanlarının sosyal medya stratejilerini optimize etmelerine ve halkla daha etkili iletişim kurmalarına yönelik öneriler sunmaktadır. Araştırma, yerel yönetim temsilcileri için sosyal medyanın stratejik kullanımının önemini vurgulamakta ve etkileşim artırıcı yöntemler hakkında değerli bilgiler sağlamaktadır.
The rise of social media has fundamentally reshaped the landscape of political discourse. This paper examines the multifaceted role of these platforms in shaping how we discuss, engage with, and understand politics across different contexts. Through a comparative analysis of case studies in the United States, India, and Brazil, we explore the diverse impacts of social media on political dynamics, considering factors such as platform design, cultural specificity, and political systems. The focus lies on exploring how social media can both democratize and polarize discourse, facilitate the spread of misinformation, and influence political processes, including elections and social movements. The paper concludes by highlighting the urgent need for multi-stakeholder approaches to address the challenges posed by social media and harness its potential for promoting informed and inclusive political participation.
Computer-mediated discussion is an increasingly popular method of engaging in political talk with other citizens. This article presents a case study of a Usenet newsgroup focused on abortion, and discusses the creation of a public sphere by the conversants. The notion of the public sphere is discussed, and measures allowing an assessment of its democratic character are proposed. A formula for estimating entropy is developed and applied to data obtained from the case study of an ongoing discussion. A high level of inequality in participation among conversants is noted, with very few of the discussants responsible for an extraordinarily high proportion of the content. This inequality, though tempered by analysis of the newsgroup on a day-to-day basis, calls into question the democratic character of the public sphere represented by this conversation.
Several theorists have entertained the possibility that the egalitarian nature of Internet-based discussion may facilitate meaningful democratic deliberative discussions amongst citizens. This article provides a preliminary empirical test of these claims by examining the extent to which blogs and blogroll communities act as forums for such deliberations. The analysis uses a dataset derived from a content analysis performed on blogs from three Canadian partisan blogrolls in October 2005. We perform three tests of the deliberative qualities of blog-based discussions: (a) whether discussion is characterized by equality of participation; (b) whether bloggers discuss substantive issues; and (c) whether bloggers engage constructively in discussions with political opponents. We find that this online discussion does exhibit some deliberative characteristics but that this discussion is often characterized by inequality of discussion, a focus on non-substantive issues, and unconstructive engagement between bloggers.
MySpace and YouTube have affected election campaigns in simple, but significant, ways. These social networking sites, which are used by a substantial segment of the U.S. voting age population, represent the next Internet generation, which is primarily user driven. They have created benefits such as increasing the potential for candidate exposure at a low cost or no cost, providing lesser known candidates with a viable outlet to divulge their message, and allowing campaigns to raise contributions and recruit volunteers online. In conjunction with these benefits, YouTube and MySpace have also posed a new set of challenges to campaign staff, the most important of which is the reduced level of control that campaigns have over the image and message of the candidate, which is of critical importance to election outcomes. This article discusses these benefits and challenges and the influence of YouTube and MySpace on the 2006 election and future campaigns.
Election campaigns are catalysts for new forms of political communication. They are a field of experimentation for new techniques and technologies to reach target groups and influence voters. This article explores how weblogs were used in the 2005 Bundestag election campaign in Germany, examining their effect on the mediatized political field. It is based on an empirical analysis of 317 campaign weblogs. Building on mediatization theory and research on campaign weblogs in other countries, weblog usage is examined along the dimensions of activity, interactivity, and connectedness. The results of the study indicate that weblog communication largely follows the dominant logic of mass media. But weblogs also offer forms of communication that allow political actors to bypass established media practices. Although the results are limited by the fact that the political blogosphere in Germany is still under construction, they reveal the potential of weblogs to enrich campaigns.
Microblogging is a new form of communication in which users can describe their current status in short posts distributed by instant messages, mobile phones, email or the Web. Twitter, a popular microblogging tool has seen a lot of growth since it launched in October, 2006. In this paper, we present our observations of the microblogging phenomena by studying the topological and geographical properties of Twitter's social network. We find that people use microblogging to talk about their daily activities and to seek or share information. Finally, we analyze the user intentions associated at a community level and show how users with similar intentions connect with each other.
We are in the midst of a technological revolution whereby, for the first time, researchers can link daily word use to a broad array of real-world behaviors. This article reviews several computerized text analysis methods and describes how Linguistic Inquiry and Word Count (LIWC) was created and validated. LIWC is a transparent text analysis program that counts words in psychologically meaningful categories. Empirical results using LIWC demonstrate its ability to detect meaning in a wide variety of experimental settings, including to show attentional focus, emotionality, social relationships, thinking styles, and individual differences.
The rise of bloggers raises the vexing question of why blogs have any influence at all, given their relatively low readership
and lack of central organization. We argue that to answer this question we need to focus on two key factors—the unequal distribution
of readers across weblogs, and the relatively high readership of blogs among journalists and other political elites. The unequal
distribution of readership, combined with internal norms and linking practices allows interesting news and opinions to rise
to the “top” of the blogosphere, and thus to the attention of elite actors, whose understanding of politics may be changed
by frames adopted from the blogosphere.
The microblogging service Twitter is in the process of being appropriated for conversational interaction and is starting to be used for collaboration, as well. In an attempt to determine how well Twitter supports user-to-user exchanges, what people are using Twitter for, and what usage or design modifications would make it (more) usable as a tool for collaboration, this study analyzes a corpus of naturally-occurring public Twitter messages (tweets), focusing on the functions and uses of the @ sign and the coherence of exchanges. The findings reveal a surprising degree of conversationality, facilitated especially by the use of @ as a marker of addressivity, and shed light on the limitations of Twitter's current design for collaborative use.
Recently there has been increasing interest in constructing general-purpose political opinion classifiers for applications in e-Rulemaking. This problem is generally modeled as a sentiment classification task in a new domain. However, the classification accuracy is not as good as that in other domains such as customer reviews. In this paper, we report the results of a series of experiments designed to explore the characteristics of political opinion expression which might affect the sentiment classification performance. We found that the average sentiment level of Congressional debate is higher than that of neutral news articles, but lower than that of movie reviews. Also unlike the adjectivecentered sentiment expression in movie reviews, the choice of topics, as reflected in nouns, serves as an important mode of political opinion expression. Manual annotation results demonstrate that a significant number of political opinions are expressed in neutral tones. These characteristics suggest that recognizing the sentiment is not enough for political opinion classification. Instead, what seems to be needed is a more finegrained model of individuals' ideological positions and the different ways in which those positions manifest themselves in political discourse.
In this paper we report research results investigating microblogging as a form of electronic word-of-mouth for sharing consumer opinions concerning brands. We ana- lyzed more than 150,000 microblog postings containing branding comments, sentiments, and opinions. We inves- tigated the overall structure of these microblog postings, the types of expressions, and the movement in positive or negative sentiment. We compared automated methods of classifying sentiment in these microblogs with man- ual coding. Using a case study approach, we analyzed the range, frequency, timing, and content of tweets in a corporate account. Our research findings show that 19% of microblogs contain mention of a brand. Of the brand- ing microblogs, nearly 20% contained some expression of brand sentiments. Of these, more than 50% were posi- tive and 33% were critical of the company or product. Our comparison of automated and manual coding showed no significant differences between the two approaches. In analyzing microblogs for structure and composition, the linguistic structure of tweets approximate the linguistic patterns of natural language expressions. We find that microblogging is an online tool for customer word of mouth communications and discuss the implications for corporations using microblogging as part of their overall marketing strategy.
This study investigates whether average political bloggers engage in four distinct activities: informing readers, checking
the media, engaging in political advocacy, and gathering money for charitable causes. Findings show that most bloggers inform
their readers about articles in the media or to draw attention to key posts on other blogs. Somewhat less frequently, most
bloggers write posts that detect errors or biases in the media. Bloggers use their blogs to pursue activist and philanthropist
activities even less often. Ideology and traffic levels slightly affect the content and purpose of blog posts.
There is good reason to believe that blogs are changing politics, but we don’t know exactly how. Nor do we know whether the
normative consequences of blogs for politics are likely to be good or bad. In this special issue, we and our co-authors undertake
the first sustained effort to map the empirical and normative consequences of blogs for politics. We begin by setting out
basic information about blogs, and some anecdotal evidence suggesting that they are indeed politically important. We go on
to identify the key empirical and normative questions that blogs raise, and discuss the dearth of relevant data in the existing
literature. We conclude by summarizing how the authors of the articles gathered in this special issue help fill this gap.
: This article examines the potential for Internet discussion boards to be a vehicle for political deliberation through a case study of the BC Votes discussion board during British Columbia’s 2001 provincial election. Research reveals that the board was dominated by a relatively small number of users and that the favourite discussion topics were not issues but how parties and leaders were performing. The authors conclude that the perception of ideological homogeneity in online discussion may be overstated and that the first post in a discussion thread has an important agenda-setting function. They also find that the relative newness of discussion boards may play a role in shaping the nature of discussion there.
Resume : Afin d’examiner le potentiel des babillards electroniques comme vehicules de debats politiques, cet article offre une etude de cas sur le babillard BC Votes lors des elections provinciales en Colombie-Britannique en 2001. Les auteurs demontrent que relativement peu d’usagers dominaient le babillard et que les sujets preferes de discussion ne touchaient pas aux enjeux de la campagne electorale, mais plutot a la performance des parties politiques et de leurs leaders. Les auteurs concluent que l’homogeneite ideologique des babillards electroniques n’est peut-etre qu’une perception exageree et que souvent le ton polemique du debat est dicte par le premier message dans un fil de discussion. Les auteurs concluent aussi que le caractere relativement nouveau des babillards reflete sur la specificite de la discussion qui s’y deroule.
Election campaigns are an extreme form of political communication. During these periods, the mass media and the electorate devote more attention to the statements of politicians and their parties than at any other time. The stakes are high, indeed; elections are at the core of the political process: the allocation of political power. Because of their importance, campaigns can act as catalysts for new forms of political communication.
Abstract This study analyzes the impact of Facebook on the 2008 presidential nomination through
In this paper, we study the linking patterns and discussion topics of political bloggers. Our aim is to measure the degree of interaction between liberal and conservative blogs, and to uncover any differences in the structure of the two communities. Specifically, we analyze the posts of 40 "A-list" blogs over the period of two months preceding the U.S. Presidential Election of 2004, to study how often they referred to one another and to quantify the overlap in the topics they discussed, both within the liberal and conservative communities, and also across communities. We also study a single day snapshot of over 1,000 political blogs. This snapshot captures blogrolls (the list of links to other blogs frequently found in sidebars), and presents a more static picture of a broader blogosphere. Most significantly, we find differences in the behavior of liberal and conservative blogs, with conservative blogs linking to each other more frequently and in a denser pattern.
Political stock markets (PSMs) are an interdisciplinary instrument
of opinion research intended to forecast election results. While
the U.S.-markets regularly outperform opinion polls, these results
could not be reproduced in Europe. In our paper we show, that this
is mainly due to systematic overpricing of smaller contracts, which
usually do not exist in the U.S. The mispricing is caused by the
market design, namely the fact, that the contracts traded in PSMs
are effectively futures, while the market mechanism is a stock market
design. Results from models deriving portfolio structure and concentration
of investment are confirmed by empirical data from PSMs.
Contrary to initial predictions Internet-mediated forms of communication have not become mediums of mass communication. Traditional
media still reach far more people than even the most popular websites. Still, there is evidence that blogs in particular help
mobilize opinions, and set the agenda for political elites such as journalists and politicians, while providing interested
citizens with a new technology of knowledge as well as a surprisingly effective way to participate in politics. This study
focuses on how the presence of blogs has altered the structure of political communication.
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All A-Twitter about the Massachusetts Senate Primary Neither Hayek nor Habermas
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