Twittering the Boko Haram Uprising in Nigeria: Investigating Pragmatic Acts in the Social Media

This paper investigates pragmatic acts in the discourse of tweeters and online feedback comments on the activities of Boko Haram, a terrorist group in Nigeria. The tweets and comments illustrate acts used to express revolutionary feelings and reflect what people say and imply in times of crisis. Tweets about Boko Haram are speech and pragmatic acts that denounce the Nigerian government, reject Western education, and call for support. Tweets and reactions from non-Muslims and nonradical Muslims condemn terrorism and denounce the terrorist group. While some tweets simply offer suggestions on how to curtail the Boko Haram insurgency, others seek the breakup of Nigeria, granting political and religious independence to the north and the southeast of the country

Armchair Detectives and the Social Construction of Falsehoods: Emergent Mob Behavior on the Internet.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Spartan Daily, March 1, 2023

Volume 160, Issue 15https://scholarworks.sjsu.edu/spartan_daily_2023/1014/thumbnail.jp

DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection

Fake News on social media platforms has attracted a lot of attention in recent times, primarily for events related to politics (2016 US Presidential elections), healthcare (infodemic during COVID-19), to name a few. Various methods have been proposed for detecting Fake News. The approaches span from exploiting techniques related to network analysis, Natural Language Processing (NLP), and the usage of Graph Neural Networks (GNNs). In this work, we propose DEAP-FAKED, a knowleDgE grAPh FAKe nEws Detection framework for identifying Fake News. Our approach is a combination of the NLP -- where we encode the news content, and the GNN technique -- where we encode the Knowledge Graph (KG). A variety of these encodings provides a complementary advantage to our detector. We evaluate our framework using two publicly available datasets containing articles from domains such as politics, business, technology, and healthcare. As part of dataset pre-processing, we also remove the bias, such as the source of the articles, which could impact the performance of the models. DEAP-FAKED obtains an F1-score of 88% and 78% for the two datasets, which is an improvement of 21%, and 3% respectively, which shows the effectiveness of the approach.Comment: Accepted at IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 202

Spartan Daily, January 29, 2019

Volume 152, Issue 2https://scholarworks.sjsu.edu/spartan_daily_2019/1001/thumbnail.jp

A Model to Measure the Spread Power of Rumors

Nowadays, a significant portion of daily interacted posts in social media are infected by rumors. This study investigates the problem of rumor analysis in different areas from other researches. It tackles the unaddressed problem related to calculating the Spread Power of Rumor (SPR) for the first time and seeks to examine the spread power as the function of multi-contextual features. For this purpose, the theory of Allport and Postman will be adopted. In which it claims that there are two key factors determinant to the spread power of rumors, namely importance and ambiguity. The proposed Rumor Spread Power Measurement Model (RSPMM) computes SPR by utilizing a textual-based approach, which entails contextual features to compute the spread power of the rumors in two categories: False Rumor (FR) and True Rumor (TR). Totally 51 contextual features are introduced to measure SPR and their impact on classification are investigated, then 42 features in two categories "importance" (28 features) and "ambiguity" (14 features) are selected to compute SPR. The proposed RSPMM is verified on two labelled datasets, which are collected from Twitter and Telegram. The results show that (i) the proposed new features are effective and efficient to discriminate between FRs and TRs. (ii) the proposed RSPMM approach focused only on contextual features while existing techniques are based on Structure and Content features, but RSPMM achieves considerably outstanding results (F-measure=83%). (iii) The result of T-Test shows that SPR criteria can significantly distinguish between FR and TR, besides it can be useful as a new method to verify the trueness of rumors

Cyberbullying Victimization and Corresponding Distress in Women of Color

Online discrimination towards women and people of color has reached epidemic levels (Fox, Cruz, & Young Lee, 2015). Any woman or person of color who uses the internet runs the risk of attracting online users who would engage them in demeaning ways. As such, it is important that researchers are able to assess and understand these experiences and the possible effects on their well-being. In Chapter 1, I conducted a systematic review of cyberbullying measures. Although studies have documented the link between cyberbullying experiences and stress (i.e., psychological distress or perceived stress), there is a need to explore factors, such as intersectional identities, that may amplify this relationship. Using minority stress theory and intersectionality theory as a guiding framework, in Chapter 2, I examined three moderators of the relationship between cybervictimization experiences and stress—namely, attributing offenses to one’s race, gender, or both (i.e., being a woman of color). Data were collected from a sample of 275 adult women of color recruited from a large urban university in the southeast and through electronic listservs and social media platforms. Results from the study revelated that cybervictimization experiences were significant and positively related to both measures of stress. My primary hypotheses were partially supported. Attributions of cybervictimization to gender or race were associated with both psychological distress and perceived stress. These results held even after controlling for neuroticism. I did not, however, find that the interaction of race and gender attributions amplified the relationship. I discuss implications for future research and practical implications for practitioners