Browsing by Subject "Social media analysis"

Now showing 1 - 6 of 6

Open Access
Misinformation detection by leveraging user communities on social media
(2024-05) Özçelik, Oğuzhan
Social media platforms have become a primary source of accessing information. However, the spread of misinformation is inevitable due to the ease of creating and sharing malicious content, including fake news. Social media users on such platforms (e.g., Twitter) often find themselves exposed to similar viewpoints and tend to avoid contrasting opinions, particularly when connected within a community. To investigate this problem, we examine the presence of user communities and leverage them as a tool to detect misinformation on social media. In this thesis, we first collect tweets together with user engagements relevant to recent events between 2020 and 2022. We then construct a human-annotated social media dataset having 5,284 English and 5,064 Turkish tweets with their veracity labels. After the data construction process, we leverage the presence of user communities for misinformation detection on social media. For this purpose, we propose a text similarity-based method that utilizes user-follower interactions within a social network to identify misinformation content. Our method first extracts important textual features of social media posts using contrastive learning. We then measure the similarity for each social media post, based on its relevance to each user in the community. Next, we train a classifier to assess the truthfulness of social media posts using these similarity scores. We evaluate our approach on three social media datasets and compare our method with the state-of-the-art approaches. The experimental results show that contrastive learning and user communities can effectively enhance the detection of misinformation on social media. Our model can identify misinformation content by achieving a consistently high weighted F1 score of over 90% across all datasets, even employing only a small number of users in communities.
Open Access
SiMiD: similarity-based misinformation detection via communities on social media posts
(IEEE, 2024-01-02) Özçelik, Oğuzhan; Toraman, C.; Can, Fazlı
Social media users often find themselves exposed to similar viewpoints and tend to avoid contrasting opinions, particularly when connected within a community. In this study, we leverage the presence of communities in misinformation detection on social media. For this purpose, we propose a similarity-based method that utilizes user-follower interactions within a social network to identify and combat misinformation spread. The method first extracts important textual features of social media posts via contrastive learning and then measures the cosine similarity per social media post based on their relevance to each user in the community. Next, we train a classifier to assess the truthfulness of social media posts using these similarity scores. We evaluate our approach on three real-world datasets and compare our method with six baselines. The experimental results and statistical tests show that contrastive learning and leveraging communities can effectively enhance the detection of misinformation on social media.
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
Stance detection: concepts, approaches, resources, and outstanding issues
(Association for Computing Machinery, 2021-07-11) Küçük, Dilek; Can, Fazlı
Stance detection (also known as stance classification and stance prediction) is a problem related to social media analysis, natural language processing, and information retrieval, which aims to de termine the position of a person from a piece of text they produce, towards a target (a concept, idea, event, etc.) either explicitly speci fied in the text, or implied only. The output of the stance detection procedure is usually from this set: {Favor, Against, None}. In this tutorial, we will define the core concepts and research problems re lated to stance detection, present historical and contemporary ap proaches to stance detection, provide pointers to related resources (datasets and tools), and we will cover outstanding issues and ap plication areas of stance detection. As solutions to stance detection can contribute to significant tasks including trend analysis, opin ion surveys, user reviews, personalization, and predictions for ref erendums and elections, it will continue to stand as an important research problem, mostly on textual content currently, and partic ularly on social media. Finally, we believe that image and video content will commonly be the subject of stance detection research soon.
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.
Open Access
Twitter sentiment analysis, 3-way classification: positive, negative or neutral?
(Institute of Electrical and Electronics Engineers Inc., 2019) Çeliktuğ, Mestan Fırat; Song, Y.; Liu, B.; Lee, K.; Abe, N.; Pu, C.; Qiao, M.; Ahmed, N.; Kossmann, D.; Saltz, J.; Tang, J.; He, J.; Liu, H.; Hu, X.
People face with the huge amount of information on each day with the advent of big data era. The data amount stored and processed by Facebook, Twitter and other big social networks store (e.g. Instagram) is massive in those days. Online social networks provide great opportunity for propagation of almost any type of information. It's actually much much easier to disseminate an idea/knowledge than previous times. Naturally, this creates information validity and immediate curiosity about mass evaluation problem in general. In this regard, sentimental polarity detection in social media (e.g.Classification of a tweet as negative or positive or neutral) is highly valuable for certain institutions, organizations. The study's main focus is to classify negative, positive and neutral approaches of three (3) annotated twitter datasets. Effect of oversampling, unigram features and other features on overall and class-based accuracy ratios is worked on the datasets. Baseline is reached in dataset-2 experiments. 88% overall accuracy was observed in dataset-1 experiments which outperforms the prior art.Unigram features has shown significant effect on overall accuracy, class-based accuracy balance.