Social media mining for identification and exploration of health-related information from pregnant women

Widespread use of social media has led to the generation of substantial amounts of information about individuals, including health-related information. Social media provides the opportunity to study health-related information about selected population groups who may be of interest for a particular study. In this paper, we explore the possibility of utilizing social media to perform targeted data collection and analysis from a particular population group -- pregnant women. We hypothesize that we can use social media to identify cohorts of pregnant women and follow them over time to analyze crucial health-related information. To identify potentially pregnant women, we employ simple rule-based searches that attempt to detect pregnancy announcements with moderate precision. To further filter out false positives and noise, we employ a supervised classifier using a small number of hand-annotated data. We then collect their posts over time to create longitudinal health timelines and attempt to divide the timelines into different pregnancy trimesters. Finally, we assess the usefulness of the timelines by performing a preliminary analysis to estimate drug intake patterns of our cohort at different trimesters. Our rule-based cohort identification technique collected 53,820 users over thirty months from Twitter. Our pregnancy announcement classification technique achieved an F-measure of 0.81 for the pregnancy class, resulting in 34,895 user timelines. Analysis of the timelines revealed that pertinent health-related information, such as drug-intake and adverse reactions can be mined from the data. Our approach to using user timelines in this fashion has produced very encouraging results and can be employed for other important tasks where cohorts, for which health-related information may not be available from other sources, are required to be followed over time to derive population-based estimates.Comment: 9 page

Assessing the extent and types of hate speech in fringe fommunities: a case study of alt-right communities on 8chan, 4chan, and Reddit

Recent right-wing extremist terrorists were active in online fringe communities connected to the alt-right movement. Although these are commonly considered as distinctly hateful, racist, and misogynistic, the prevalence of hate speech in these communities has not been comprehensively investigated yet, particularly regarding more implicit and covert forms of hate. This study exploratively investigates the extent, nature, and clusters of different forms of hate speech in political fringe communities on Reddit , 4chan , and 8chan . To do so, a manual quantitative content analysis of user comments ( N  = 6,000) was combined with an automated topic modeling approach. The findings of the study not only show that hate is prevalent in all three communities (24% of comments contained explicit or implicit hate speech), but also provide insights into common types of hate speech expression, targets, and differences between the studied communities

Characterizing and Detecting Hateful Users on Twitter

Most current approaches to characterize and detect hate speech focus on \textit{content} posted in Online Social Networks. They face shortcomings to collect and annotate hateful speech due to the incompleteness and noisiness of OSN text and the subjectivity of hate speech. These limitations are often aided with constraints that oversimplify the problem, such as considering only tweets containing hate-related words. In this work we partially address these issues by shifting the focus towards \textit{users}. We develop and employ a robust methodology to collect and annotate hateful users which does not depend directly on lexicon and where the users are annotated given their entire profile. This results in a sample of Twitter's retweet graph containing $100,386$ users, out of which $4,972$ were annotated. We also collect the users who were banned in the three months that followed the data collection. We show that hateful users differ from normal ones in terms of their activity patterns, word usage and as well as network structure. We obtain similar results comparing the neighbors of hateful vs. neighbors of normal users and also suspended users vs. active users, increasing the robustness of our analysis. We observe that hateful users are densely connected, and thus formulate the hate speech detection problem as a task of semi-supervised learning over a graph, exploiting the network of connections on Twitter. We find that a node embedding algorithm, which exploits the graph structure, outperforms content-based approaches for the detection of both hateful ($95\%$ AUC vs $88\%$ AUC) and suspended users ($93\%$ AUC vs $88\%$ AUC). Altogether, we present a user-centric view of hate speech, paving the way for better detection and understanding of this relevant and challenging issue.Comment: This is an extended version of the homonymous short paper to be presented at ICWSM-18. arXiv admin note: text overlap with arXiv:1801.0031

HATE CRIMES IN SOCIAL MEDIA: A CRIMINOLOGICAL REVIEW

Hate crime in social media is a common phenomenon around the world. Hate crime against different races, minorities, and ethnic people or groups is now spreading via social media platforms in form of hate speech. The anonymity of the internet user and the availability of the internet make this crime very easy to commit by the offender. This paper aims to find out the targets of hate crime in social media and its effect on the victims. The people who are victimized by hate crime in social media because of their race, gender especially female and religious minority. The study has done by secondary data analysis. Hate crime in social media has a devastating effect on the victim both physically and mainly psychologically which makes them mentally inferior, degradation of self-esteem, and also create a fear of violence in their mind. The existing laws against hate crime in social media should be implemented more precise way and different types of detection methods should be applied to detect the offenders. This paper can be helpful to increase awareness about hate crime in social media which is unnoticed by many researchers in our country and can lead a way to stop victimization in social media platforms

KONTRIBUSI LITERASI DIGITAL PADA SISWA TERHADAP HATE SPEECH DI MEDIA SOSIAL INSTAGRAM (STUDI DESKRIPTIF KORELASIONAL PADA SISWA KELAS 2 SMAN 1 BANDUNG)

Penelitian ini dilatarbelakangi oleh komunikasi massa dan pengambilan informasi di media sosial instagram menimbulkan adanya sikap hate speech (ujaran kebencian) dalam masyarakat. Dengan adanya tingkat kriminalitas yang tinggi akibat tidak bijak dalam menggunakan media sosial instagram, diperlukan penguasaan kompetensi literasi digital terutama di kalangan remaja. Saat ini, penggunaan media sosial instagram sangat popular, didukung adanya konten dalam bentuk foto dan video, serta filter menarik, tetapi jika pengguna tidak memperhatikan aturan dalam berinternet, maka akan merugikan berbagai pihak. Hadirnya kompetensi literasi digital memberi pemahaman kepada remaja pengguna media sosial instagram agar dapat mengevaluasi dan berpikir kritis dalam bertindak. Masalah yang dikaji dalam penelitian ini yaitu apakah terdapat hubungan antara literasi digital siswa kelas 2 SMAN 1 Bandung terhadap Hate Speech di media sosial Instagram. Penelitian ini menggunakan pendekatan kuantitatif dengan metode deskriptif korelasional, teknik pengambilan sampel yaitu menggunakan purposive sampling dengan jumlah 20 orang siswa, serta data dikumpulkan menggunakan kuesioner. Analisis data yang digunakan adalah non parametrik test dengan uji korelasi Kendall-Tau. Berdasarkan hasil penelitian, menunjukan bahwa terdapat hubungan yang cukup dan signifikan antara Literasi Digital siswa dan persepsi mengenai Hate Speech di media sosial Instagram. Secara Khusus hasil penelitian pada Literasi Digital siswa berada dalam kategori Baik dan mengenai persepsi Hate Speech di Media Sosial Instagram berada dalam kategori Sangat Baik. Kata Kunci: Komunikasi Massa, Literasi Digital, Hate Speech, Media Sosial Instagram This research is motivated by mass communication and retrieval of information on Instagram social media giving rise to a hate speech attitude in society. With the high crime rate due to unwise in using social media Instagram, the mastery of digital literacy competencies is needed especially among teenagers. Currently, the use of Instagram social media is very popular, supported by content in the form of photos and videos, as well as interesting filters, but if the user does not pay attention to the rules in the internet, it will be detrimental to various parties. The presence of digital literacy competencies gives teenage users of Instagram social media the ability to evaluate and think critically in acting. The problem examined in this study is whether there is a relationship between digital literacy of class 2 students of SMAN 1 Bandung and Hate Speech on Instagram social media. This research uses a quantitative approach with a correlational descriptive method, the sampling technique is using purposive sampling with a total of 20 students, and data is collected using a questionnaire. Analysis of the data used is non-parametric test with Kendall-Tau correlation test. Based on research results, it shows that there is a significant and significant relationship between students' Digital Literacy and perceptions about Hate Speech on social media Instagram. Specifically the results of research on Digital Literacy students are in the Good category and regarding the perception of Hate Speech on Social Media Instagram is in the Very Good category. Keywords: Mass Communication, Digital Literacy, Hate Speech, Social Media Instagra

Hate Speech, Emotions, and Gender Identities : A Study of Social Narratives on Twitter with Trainee Teachers

The objective of this study is, on the one hand, to analyse emotional responses to the construction of hate speech relating to gender identity on Twitter. On the other hand, the objective is to evaluate the capabilities of trainee primary education teachers at constructing alternative counter-narratives to this socially alive issue, surrounding the approval of the Ley de Identidad de Género [Gender Identity Law] in Chile, in 2018. With this two-fold objective in mind, quantitative, descriptive, and inferential analysis and qualitative analysis techniques are all applied. The results inform us of the influence of socially constructed emotions and feelings that are expressed in social narratives. However, the narratives of the participants neither appeared to reach satisfactory levels of reflection on the social issues that stirred their own emotional responses, nor on the conflict between reason and the value judgements that they expressed in the digital debate (counter-narratives). These results point to the need to consider both emotions and feelings, as categories of social analysis, and to reflect on their forms of expression within the framework of education for inclusive democratic citizenship

Positive Gender Responses to Hate Speech of ‘The Little Mermaid’ on Twitter: Critical Discourse Analysis

Hate speech indicates hatred, prejudice, or discrimination toward individuals or groups due to appearance, race, religion, etc. Hate speech quickly occurs on social media; everyone can be the victim, like Disney posted about Little Mermaid on Twitter. People filling the comment section of Disney posted with the reactions, including hate speech and positive responses toward Halle as the character of Ariel. This study aims to find the different positive responses of women and men toward hate speech using women's and men's language features, stereotyped language that appears in a positive response, present the category of hate speech, and why people spread hate in The Little Mermaid post. The research uses the Critical Discourse Analysis approach by Van Dijk (1995) and Fowler (1991). The data were analyzed using the qualitative research method and presented with descriptive text. The data object is taken from the two Tweets Disney posted about The Little Mermaid on Twitter. The result proved that there is a different way when women and men respond positively to hate speech through women's and men's features but the positive responses of females are more polite than males. Although in positive responses, this study showed that stereotyped language of perceived category essentialism appeared, focused on the term of race. This study presented only seven categories of hate speech that occurred and why people spread hate to convey their disappointment with Disney's decision that the main actress did not meet the expectation like in animation. Keywords: hate speech, critical discourse analysis, gender, positive response