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Online social media has created new paradigms of information sharing which not only provides appropriate platform for the contributors but also for active information seekers. Numerous forms of social media have gained the widespread attention internet users’ almost on explosion level. Availability of data on such behemoth scale mandates regular an...
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... cultivates an inventive kind of data science which is well versed in social and computational theories, specialized to analyze unruly social media data [46], and skilled to help bridge the gap from what we want to know about the infinite social media world with computational tools. According to [61] the internet traffic of September, 2016 social media websites can be classified as describes in Table 4. Twitter 10 ...Similar publications
Suicide is one of the major causes of death across the world. With data being generated in humongous quantity every second through various media like social networking sites, surveys, etc.; a lot of relevant information is available for suicide analysis. Data from social networking sites especially twitter has been extensively considered for resear...
Social media represents a rich environment to collect huge amounts of data containing useful information about people’s behaviors and interactions. In particular, such information has been widely exploited for analyzing the mobility of people, as geotagged social media posts allow to extract accurate patterns on movements of people. This paper pres...
Social media has become one of the primary sources of data which available for policy analysts and policymakers. As the evidence, active Twitter users are sending 500 million tweets per day containing thoughts, opinions, pictures, and other information. Social media offers new challenges related to how the data is acquired and how to analyze it. Un...
When the world’s countries agreed on the 2030 Agenda for Sustainable Development, they recognized that equity and inclusion should be at the center of implementing the 17 Sustainable Development Goals (SDGs). SDG 15, which calls for protecting, restoring, and promoting the sustainable use of terrestrial ecosystems, has spurred commitments to restor...
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... Text mining, also known as text analytics or natural language processing (NLP), involves extracting meaningful insights and patterns from unstructured text data. Social media platforms generate vast amounts of text data in posts, comments, tweets, and messages, making text-mining techniques invaluable for analyzing and understanding social media content [39][40][41]. ...
For over three decades, the European Union’s (EU) transport policy has aimed at fostering environmental sustainability and energy efficiency. Since 2015, European policymakers have focused more on three key sustainable development goals: decarbonizing the transport system, promoting low-emission mobility solutions, and transitioning to renewable and alternative fuels. To effectively communicate priorities and engage stakeholders, EU policymakers regularly use social media platforms like Twitter (now known as X). This active discourse involves policymakers, industrial stakeholders, the media, and the public, offering insights into the role of transport policy in addressing climate change and energy transition challenges. The current research endeavors to track and analyze the evolution of user-generated content related to climate change, energy transition, and smart mobility on Twitter from 2011 to 2021. This research uses text-mining and social network analysis techniques to quantitatively and qualitatively assess the dynamics of relevant EU policies and their effects. The study’s findings can be used to establish a robust monitoring and evaluation framework at the EU and national levels. This framework will assess the effectiveness of communicating strategic priorities for sustainable transport development. It also holds potential for application in other sectors, broadening its impact.
... Also, short texts are in the focus of such a relatively new phenomenon as memes (Dawkins, 2016;Memetics, 2001). Interest in this issue grows in the framework of interdisciplinary studies of social networks communications (Dizikes, 2014;Hu & Liu, 2012;Singh et al., 2017). One of the niche research areas is concerned with the literary segment "small prose" (Flash fiction, 2004). ...
Purpose. An empirical verification of the Hemingway’s “sad hypothesis” and study of some individual characteristics of a discourse formation in a process of short texts understanding. Methods and procedure of research. The study was based on the principle of a standardized interview, which was carried out on a random sample (103 respondents) using the questionnaire. The subjects interpreted two proverbs and the short story by Hemingway (“For sale: baby shoes, never worn”). In each case, it was proposed to choose one of the six ready-made interpretations or to create an original one. Proverb explications were classified by experts as “normative” or “deviating”, and interpretations of the story were evaluated into “sad” or “pragmatic” ones. Also, a “normativity index” was calculated for each respondent, reflecting the number of normative renditions of proverbs. The Psychogeometric test was used, and such socio-demographic characteristics were recorded as: gender, age, having children. Results. This study refutes the “sad hypothesis” regarding Hemingway’s six-word story affect. The prevalence of pragmatic type interpretation over sentimental one is statistically significant. The type of interpretation turned out to be not directly related to any of the considered socio-demographic characteristics. It was found that the sad interpretation of the story reliably corresponds to a high normativity of the proverbs explications. Conversely, respondents with deviating interpretations of proverbs were significantly more likely to interpret Hemingway’s story in a pragmatic way. Differential psychological features, which were distinguished using the Psychogeometric test, turned out to be an insignificant predictor of the six-word story interpretations. Conclusions. The analysis of the research results made it possible to argue the thesis that the differences in the formation of individual discourses are directly related to a worldview and indirectly determined by other factors in turn influencing the outlook. The interaction of the worldview with discursive practice that arises in the process of short texts understanding is carried out according to differential scenarios. These scenarios are conditioned by such individual characteristics as discursive conformity and discursive lability, which, in turn, correspond with high and low normativity, respectively. Above features are cognitive in nature. Their ontological localization coincides with the I-language (Chomsky).
... In the text stream, it is quite challenging to expect prompt words due to the unstructured nature of stream text, and it is quite complex to recognize the features of unstructured text. Text in social stream contains various symbols (@, #, etc.) [15], short forms (hlo, hru, luv, etc.), which make social text noisy and challenging [19,20]. Multiple approaches claim to solve this problem of text pre-processing in which supervised and unsupervised techniques are available. ...
Taming data will always be a significant challenge in online social networks. These networks are rapidly becoming the emerging source for users to explore the primary sources to seek information in the form of events. Rich informational data can be extracted from various social platforms like twitter text streams for direct insights into enduring topics and classifying them based on their similarities. To address the research issues of event detection and classification, we model events as evolving clusters over a period of time. The inability of conventional clustering algorithms to process the data streams mandates the use of a fast yet robust method. Therefore this work employs quick comparisons of data coming from social streams relying on a twin network known as the Siamese network, which can detect the novel event based on clustering by comparing their content dependent feature. We also trained dataset derived from the social text stream from twitter and other sources, where embedding encode every word representation mapped to a vector. This representation of word into real valued vectors provides a specific processing task for event classification. Finally, we compared the proposed technique with the existing methods, and the results obtained through several experiments are a clear indicator of the efficacy of the proposed scheme.
... Identify the features of the unstructured text are quite tricky. Text in social stream contains various symbols (@, #, etc.) [11], short forms (hlo, hru, luv, etc.), which make social text noisy and challenging [15,16]. Cluster based approaches for event detection and classification task for online incremental clustering in [17] are used. ...
Gigantic growth of social media and unbeatable trend of progress in the direction of the web seeking user’s interests have generated a storm of social text streams. Seeking information to know the phenomenon of various events in the early stages is quite interesting. Various kinds of social media live streams attract users to participate in real-time events to become a part of an immense crowd. However, the vast amount of text is present on social media, the unnecessary information bogs a social text stream filtering to extract the appropriate topics and events effectively. Therefore, detecting, classifying, and identifying burst events is quite challenging due to the sparse and noisy text of Twitter. The researchers' significant open challenges are the effective cleaning and profound representation of the text stream data. This research article's main contribution is to provide a detailed study and explore bursty event detection in the social text stream. Thus, this work's main motive is to present a concise approach that classifies and detects the event keywords and maintains the record of the event based on related features. These features permit the approach to successfully determine the booming pattern of events scrupulously at different time span. Experiments are conducted and compared with the state-of-the-art methods, which reveals that the proposed approach is proficient to detect valuable patterns of interest and also achieve better scoresto extract burst events on social media posted by various users.
... SMs have applications in a broad range of text mining and NLP applications, like text summarization (Schubert, 2015) (Yan et al., 2015), machine translation (Pal et al. 2012) (Rinaldi, 2008) document classification (Zhou et al., 2016) (Niraula et al., 2015) (Huang et al., 2016) (Navigli et al., 2011) (Chen et al., 2014) and retrieval (Schubert, 2015) (Yan et al., 2015) (Rinaldi, 2008), information extraction (Soderland, 1999), question-answering (Zhou et al., 2016), semantic similarity applications (Niraula et al., 2015) (Huang et al., 2016), word sense disambiguation (Navigli et al., 2011), (Chen et al., 2014) web search (Shen et al., 2014) and social media mining (Singh et al., 2017). They enable to take advantage of the knowledge encompassed in unstructured/semi-structured texts corpora and KRs to compare things. ...
Cosine similarity compares two units of text to get the semantic relation between them. This comparison is based on the numerical value (features) represented by semantic vectors. Orthogonality between the feature vectors makes them inefficient for semantic comparisons. Modifying the metrics to handle orthogonality performs better taking the advantage of representations. This article proposed modified cosine similarity metrics for comparing sentences based on multi-feature embedding vectors. Our approach relies on the assumption that linguistic units may have multiple aspects of semantics which should be considered while calculating the similarity between the two units.
... Word embeddings have been successfully implemented in language models, extractive summarization, machine translation, named entity recognition, disambiguation, parsing, semantic word cloud and social media mining [17]. ...
Automatic text mining processes and other sophisticated natural language processing constructs need realistic representations of text/documents which embed semantics efficiently. All the representations work on the notion that every data contains different explanatory factors (attributes). In this article, we exploit these explanatory factors to study and compare various semantic representation methods for text documents. The article critically reviews recent trends in the area of semi-supervised semantic representations, covering cutting-edge methods in distributed representations such as embeddings. This article gives a broad and synthesized description of various forms of text representations, presented in their chronological order ranging from BoW models to the most recent embeddings learning. Conclusively, various findings taken together provide valuable pointers for researchers looking to work in the field of semantic representations. In addition, the article also shows that one need to develop a model for learning universal embeddings in unsupervised/semi-supervised settings that incorporate contextual as well as word-order information, with language independent features and which would be feasible for large dataset.
It is evaluated that around 80% of all data is unstructured, with content being one of the most widely recognized sorts of unstructured information. In view of the untidy idea of content, dissecting, understanding, arranging, and figuring out content information is hard and tedious so most organizations neglect to extricate an incentive from that. This is the place content arrangement with AI steps in. By utilizing content classifiers, organizations can structure business data, for example, email, authoritative records, site pages, visit discussions, and online life messages in a quick and practical manner. This permits organizations to spare time while examining content information, help illuminate business choices, and robotize business forms. In current years, there has been an exponential development in the quantity of complex records and messages that require a more profound comprehension of AI strategies to have the option to precisely group messages in numerous applications. Many AI approaches have accomplished outperforming brings about common language preparing. The achievement of these learning calculations depends on their ability to comprehend complex models and non-straight connections inside information. In this article we are going to discuss a machine learning based text classifier: Native Bayes.
Social-media and blogs are increasingly used for social-communication, an idea and thought publishing platform. Public intentions, wisdom, problems, solutions, mental states are shared in social media. Text is being the best and the most common way to communicate over social networks. All kinds of data shared in social sites like Facebook, Twitter, and Microblogs. People from different pursuance uses these media to publish thoughts and convey messages through text. Consequently, occurrences in social life are rapidly discussed in social blogs in daily manner. This work aims at discovering ongoing social crisis from the Twitter data. Text mining technique and sentiment analysis were applied to detect the current social crisis from the social sites. Twitter data were collected to identify the recent social crisis. Furthermore, the identified crisis was compared to reputed newspapers. A hybrid method used to detect recent social issues resulted nicely. However, our proposed analysis shows identifying rate 89%, 95%, 83%, 53%, and 98% for the top 5 identified crisis accordingly in the date between 27 February and 11 March 2020. The strategy used in this study for the detection of recent social crisis will contribute to the social life and findings of crisis will be eliminated easily.
Despite of the behemoth utilization of social media platforms for various aspects, which provides opportunities to analyze and study the social behavior of users, text mining's role has been not explored fully. For this, text mining is the way to discover interesting patterns in data. The motive of text mining is to utilize discovered patterns to elucidate contemporary behavior or to predict future outcomes. Multiple disciplines participate in crawling text to discover required textual patterns such as mathematical modeling, computer science, data mining and warehousing to name a few. For this purpose, embeddings are also playing a key role and under the umbrella of machine learning, IoT (Internet of things) are coping up flawlessly at an individual level to predict the behavior in terms of security privacy, analysis, and prediction. Through this chapter, explaining the role of such strategies in social media text analysis for finding knowledgeable patterns. To illustrate and deliberate the areas of social media which are reachable on an amazing variety in the field of text mining using IoT‐enabled services in terms of machine learning are also described. Outcomes can provide as a baseline for future of IoT research based on machine learning in emerging applications.
