A short review on susceptibility to falling for fake political news

This review discusses recent findings on individuals’ susceptibility to falling for fake news in the political context. Considering political attitudes and analytical thinking, we find that individuals tend to overrate the accuracy of true and fake political news that are consistent with their own political attitudes. This tendency, however, cannot be explained by motivated reasoning. This is supported by findings showing that analytical thinking is negatively related to susceptibility to falling for fake news, regardless of whether they are consistent or inconsistent with one’s political attitudes. We suggest that future works should aim at i) examining how, for example, news consumption habits relate to susceptibility to falling for fake news and ii) implementing other, more external valid fake news tests

InfoInternet for Education in the Global South: A Study of Applications Enabled by Free Information-only Internet Access in Technologically Disadvantaged Areas (authors' version)

This paper summarises our work on studying educational applications enabled by the introduction of a new information layer called InfoInternet. This is an initiative to facilitate affordable access to internet based information in communities with network scarcity or economic problems from the Global South. InfoInternet develops both networking solutions as well as business and social models, together with actors like mobile operators and government organisations. In this paper we identify and describe characteristics of educational applications, their specific users, and learning environment. We are interested in applications that make the adoption of Internet faster, cheaper, and wider in such communities. When developing new applications (or adopting existing ones) for such constrained environments, this work acts as initial guidelines prior to field studies.Comment: 16 pages, 1 figure, under review for a journal since March 201

Detecting Textual Propaganda Using Machine Learning Techniques

سيطرت الشبكات الاجتماعية على العالم بأسره من خلال توفير منصة لنشر المعلومات. عادة ما يشارك الناس المعلومات دون معرفة صدقها. في الوقت الحاضر ، تُستخدم الشبكات الاجتماعية لاكتساب النفوذ في العديد من المجالات مثل الانتخابات والإعلانات وما إلى ذلك ، وليس من المستغرب أن تصبح وسائل التواصل الاجتماعي سلاحًا للتلاعب بالمشاعر من خلال نشر معلومات مُضللة. الدعاية هي إحدى المحاولات المنهجية والمتعمدة التي تستخدم للتأثير على الناس لتحقيق مكاسب سياسية ودينية. في هذه الورقة البحثية ، تم بذل جهود لتصنيف النص الدعائي من النص غير الدعائي باستخدام خوارزميات التعلم الآلي الخاضعة للإشراف. تم جمع البيانات من مصادر الأخبار في الفترة من يوليو 2018 إلى أغسطس 2018. بعد إضافة التعليقات التوضيحية على النص ، يتم تنفيذ هندسة الميزات باستخدام تقنيات مثل مصطلح تردد / تردد الوثيقة العكسي (TF / IDF) وحقيبة الكلمات (BOW). يتم توفير الميزات ذات الصلة لدعم المصنفات المتجهة (SVM) و Multinomial Naïve Bayesian (MNB). يتم إجراء ضبط دقيق لـ SVM عن طريق أخذ kernel Linear و Poly و RBF. أظهر SVM نتائج أفضل من MNB من خلال دقة 70٪ واسترجاع 76.5٪ ودرجة F1 69.5٪ ودقة كلية 69.2٪.Social Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising that social media has become a weapon for manipulating sentiments by spreading disinformation.  Propaganda is one of the systematic and deliberate attempts used for influencing people for the political, religious gains. In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annotating the text, feature engineering is performed using techniques like term frequency/inverse document frequency (TF/IDF) and Bag of words (BOW). The relevant features are supplied to support vector machine (SVM) and Multinomial Naïve Bayesian (MNB) classifiers. The fine tuning of SVM is being done by taking kernel Linear, Poly and RBF. SVM showed better results than MNB by having precision of 70%, recall of 76.5%, F1 Score of 69.5% and overall Accuracy of 69.2%

Attitude of Library and Information Science Students of Imo State University, Owerri towards the Utilization of Social Media.

This study investigated the attitude of Library and Information Science students of Imo State University, Owerri towards the utilization of social media. The study adopted a survey research design. The population of the study is 370. This is made up of all the undergraduates from 100 to 400 level in the Department of Library and information Science, Imo State University. The instrument used to collect data was Google form. This was designed by the researchers and sent to the course representatives of the various levels who were instructed to post it on their class WhatsApp group platform. Reminders were sent every three days for a period of two (2) weeks to those who are yet to complete the instrument. At the expiration of two weeks given for the completion of the instrument, 306 undergraduates responded showing 83% response rate. Analyses were done based on that. Data collected were analysed using frequencies and percentages. The study revealed that: the means of accessing the internet by Library and Information Science students of Imo State University, Owerri are through data subscription using their phones; the social media mostly used by the students are WhatsApp, Facebook, YouTube and Telegram; the students’ purpose of using social media are social interaction, communicating with family members, updating knowledge, getting news and academic purposes; the students read and repost the social media contents they receive and they verify the social media contents forwarded to them if they are real or fake to a low extent

Addressing Health Misinformation Dissemination on Mobile Social Media

With the pervasive use of social media apps, it is now common to see that people share health related information on the mobile social platforms. The spread of health misinformation on social media apps such as Facebook and WeChat poses serious threats to individual and public health. To address this issue, we drew upon reflective-impulsive model and went beyond the traditional view of users as reasoned decision makers by arguing that the health misinformation dissemination on social media apps is primarily driven by the impulsive system (habit and avoidance orientation). To reduce the dissemination, the reflective system should be strengthened. Accordingly, we propose that the presence of a message which emphasizes the negative effects of health misinformation dissemination and/or the accountability for health misinformation dissemination will reduce users’ dissemination of the misinformation. Situational factors such as time availability, environmental noisiness and the dispositional moderator trait mindfulness will moderate the intervention effects