Browsing by Subject "Twitter"

Now showing 1 - 7 of 7

Open Access
Aspect based opinion mining on Turkish tweets
(2012) Akbaş, Esra
Understanding opinions about entities or brands is instrumental in reputation management and decision making. With the advent of social media, more people are willing to publicly share their recommendations and opinions. As the type and amount of such venues increase, automated analysis of sentiment on textual resources has become an essential data mining task. Sentiment classification aims to identify the polarity of sentiment in text. The polarity is predicted on either a binary (positive, negative) or a multi-variant scale as the strength of sentiment expressed. Text often contains a mix of positive and negative sentiments, hence it is often necessary to detect both simultaneously. While classifying text based on sentiment polarity is a major task, analyzing sentiments separately for each aspect can be more useful in many applications. In this thesis, we investigate the problem of mining opinions by extracting aspects of entities/topics on collection of short texts. We focus on Turkish tweets that contain informal short messages. Most of the available resources such as lexicons and labeled corpus in the literature of opinion mining are for the English language. Our approach would help enhance the sentiment analyses to other languages where such rich sources do not exist. After a set of preprocessing steps, we extract the aspects of the product(s) from the data and group the tweets based on the extracted aspects. In addition to our manually constructed Turkish opinion word list, an automated generation of the words with their sentiment strengths is proposed using a word selection algorithm. Then, we propose a new representation of the text according to sentiment strength of the words, which we refer to as sentiment based text representation. The feature vectors of the text are constructed according to this new representation. We adapt machine learning methods to generate classifiers based on the multi-variant scale feature vectors to detect mixture of positive and negative sentiments and to test their performance on Turkish tweets.
Open Access
Discovering influencers in opinion formation over social graphs
(Institute of Electrical and Electronics Engineers , 2023-03-23) Shumovskaia, V.; Kayaalp, M.; Cemri, Mert; Sayed, A. H.
The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observations and exchange the beliefs with their neighbors. In this work, it is shown how the sequence of publicly exchanged beliefs over time allows users to discover rich information about the underlying network topology and about the flow of information over the graph. In particular, it is shown that it is possible (i) to identify the influence of each individual agent to the objective of truth learning, (ii) to discover how well-informed each agent is, (iii) to quantify the pairwise influences between agents, and (iv) to learn the underlying network topology. The algorithm derived herein is also able to work under non-stationary environments where either the true state of nature or the graph topology are allowed to drift over time. We apply the proposed algorithm to different subnetworks of Twitter users, and identify the most influential and central agents by using their public tweets (posts).
Open Access
Occupation of Twitter during gezi movement in Turkey
(2015) Kutay, Can
This thesis will attempt to analyze the occupation of social networking site Twitter by Gezi protestors just like any other offline public place like Gezi Park and the deoccupation process of the same platform by the Pro-government users thus aiming to break the linkage between “on” and” off” during Gezi protests in Turkey. Throughout this struggle between opposite collectivities, the social networking sites such as Twitter were defined as contested spaces that could be “freed” from the protestors’ occupation like any offline public place.
Open Access
Rise of citizen journalism: content analysis on Turkish Twittersphere
(2022-05) Özbasa, Özen Ayşe
With the advancement of technology, increasing use of social media, and declining trust in mainstream media, which is dominated by moguls, as well as the Covid 19 pandemic outbreak which has taken hold of the world, various journalism practices have undergone some changes. In this process, ordinary citizens have sought alternatives; the citizen journalism concept in digital media platforms has emerged and started to be used progressively. This thesis focuses on the Turkish Twittersphere and delves into the influences of citizen journalism, which has eventually led to mainstream media outlets’ presence on the Twittersphere. Ordinary citizens engage in plentiful debates and discussions by contributing to overall interaction, whether active or passive. In that context, mixed content analysis is employed by retrieving data in four up-to-date categories, namely health, climate, economy, and Europeanization. Coding data according to engagement rate and several other categories, the findings of this study reveal that citizen journalism is on the up and has gained the trust of other users in the Turkish Twittersphere compared to prominent traditional media outlets. Thus, in addition to highlighting why citizen journalism has emerged and gained prominence to such a degree, this thesis also demonstrates the declining credibility of mainstream media and its reflections.
Open Access
Stance detection: a survey
(Association for Computing Machinery, 2020) Küçük, D.; Can, Fazlı
Automatic elicitation of semantic information from natural language texts is an important research problem with many practical application areas. Especially after the recent proliferation of online content through channels such as social media sites, news portals, and forums; solutions to problems such as sentiment analysis, sarcasm/controversy/veracity/rumour/fake news detection, and argument mining gained increasing impact and significance, revealed with large volumes of related scientific publications. In this article, we tackle an important problem from the same family and present a survey of stance detection in social media posts and (online) regular texts. Although stance detection is defined in different ways in different application settings, the most common definition is “automatic classification of the stance of the producer of a piece of text, towards a target, into one of these three classes: {Favor, Against, Neither}.” Our survey includes definitions of related problems and concepts, classifications of the proposed approaches so far, descriptions of the relevant datasets and tools, and related outstanding issues. Stance detection is a recent natural language processing topic with diverse application areas, and our survey article on this newly emerging topic will act as a significant resource for interested researchers and practitioners.
Open Access
Temporal workload-aware replicated partitioning for social networks
(Institute of Electrical and Electronics Engineers, 2014-11) Turk, A.; Selvitopi, R. O.; Ferhatosmanoglu, H.; Aykanat, Cevdet
Most frequent and expensive queries in social networks involve multi-user operations such as requesting the latest tweets or news-feeds of friends. The performance of such queries are heavily dependent on the data partitioning and replication methodologies adopted by the underlying systems. Existing solutions for data distribution in these systems involve hash- or graph-based approaches that ignore the multi-way relations among data. In this work, we propose a novel data partitioning and selective replication method that utilizes the temporal information in prior workloads to predict future query patterns. Our method utilizes the social network structure and the temporality of the interactions among its users to construct a hypergraph that correctly models multi-user operations. It then performs simultaneous partitioning and replication of this hypergraph to reduce the query span while respecting load balance and I/O load constraints under replication. To test our model, we enhance the Cassandra NoSQL system to support selective replication and we implement a social network application (a Twitter clone) utilizing our enhanced Cassandra. We conduct experiments on a cloud computing environment (Amazon EC2) to test the developed systems. Comparison of the proposed method with hash- and enhanced graph-based schemes indicate that it significantly improves latency and throughput.
Open Access
A tutorial on stance detection
(Association for Computing Machinery, Inc, 2022) Küçük, Dilek; Can, Fazlı
Stance detection (also known as stance classification, stance prediction, and stance analysis) is a problem related to social media analysis, natural language processing, and information retrieval, which aims to determine the position of a person from a piece of text they produce, towards a target (a concept, idea, event, etc.) either explicitly specified in the text, or implied only. Common stance classes include Favor, Against, and None. In this tutorial, we will define the core concepts and other related research problems, present historical and contemporary approaches to stance detection (including shared tasks and tools employed), provide pointers to related datasets, and cover open research directions and application areas of stance detection. As solutions to stance detection can contribute to diverse applications including trend analysis, opinion surveys, user reviews, personalization, and predictions for referendums and elections, it will continue to stand as an important research problem, mostly on textual content currently, and particularly on Web content including social media.