Marites Culture in the Philippines: An Emergent Online Gossip Phenomenon

This research article explores the emergence and characteristics of Marites culture in the Philippines, specifically focusing on its role in shaping social relations and communication during the pandemic and the present. Utilizing literature review approach and sociological perspectives, the study analyzes media reports and online sources to investigate the origins, features, and implications of Marites culture. The research emphasizes the significance of Marites culture as a reflection of broader social and cultural trends in the Philippines, including the increasing importance of online communication and the influence of traditional gossip practices. Moreover, the study examines the potential advantages and disadvantages of Marites culture, such as its ability to disseminate information and shape public opinion, as well as its potential to spread misinformation and trigger social tensions. The findings underscore the necessity for a critical and nuanced understanding of emerging cultural phenomena, considering their historical, social, and cultural contexts and implications

Three contextual dimensions of information on social media: lessons learned from the COVID-19 infodemic

The COVID-19 pandemic has been accompanied on social media by an explosion of information disorders such as inaccurate, misleading and irrelevant information. Countermeasures adopted thus far to curb these informational disorders have had limited success because these did not account for the diversity of informational contexts on social media, focusing instead almost exclusively on curating the factual content of user’s posts. However, content-focused measures do not address the primary causes of the infodemic itself, namely the user’s need to post content as a way of making sense of the situation and for gathering reactions of consensus from friends. This paper describes three types of informational context—weak epistemic, strong normative and strong emotional—which have not yet been taken into account by current measures to curb down the informational disorders. I show how these contexts are related to the infodemic and I propose measures for dealing with them for future global crisis situations.Ethics & Philosophy of Technolog

The retransmission of rumor and rumor correction messages on Twitter

This article seeks to examine the relationships among source credibility, message plausibility, message type (rumor or rumor correction) and retransmission of tweets in a rumoring situation. From a total of 5,885 tweets related to the rumored death of the founding father of Singapore Lee Kuan Yew, 357 original tweets without an “RT” prefix were selected and analyzed using negative binomial regression analysis. The results show that source credibility and message plausibility are correlated with retransmission. Also, rumor correction tweets are retweeted more than rumor tweets. Moreover, message type moderates the relationship between source credibility and retransmission as well as that between message plausibility and retransmission. By highlighting some implications for theory and practice, this article concludes with some limitations and suggestions for further research.MOE (Min. of Education, S’pore)Accepted versio

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

Examining the Impact of Emojis on Disaster Communication: A Perspective from the Uncertainty Reduction Theory

Communication is a purposeful process, especially during disasters, when emergency management officials and citizen journalists attempt to disseminate relevant information to as many affected people as possible. X (previously Twitter), a popular computer-mediated communication (CMC) platform, has become an essential resource for disaster information given its ability to facilitate real-time communication. Past studies on disasters have mainly concentrated on the verbal-linguistic conventions of words and hashtags as the means to convey disaster-related information. Little attention has been given to non-verbal linguistic cues, such as emojis. In this study, we investigate the use of emojis in disaster communication on X by using uncertainty reduction theory as the theoretical framework. We measured information uncertainty in individual tweets and assessed whether information conveyed in external URLs mitigated such uncertainty. We also examined how emojis affect information uncertainty and information dissemination. The statistical results from analyzing tweets related to the 2018 California Camp Fire disaster show that information uncertainty has a negative impact on information dissemination, and the negative impact was amplified when emojis depicted items and objects instead of facial expressions. Conversely, external URLs reduced the negative impact. This study sheds light on the influence of emojis on the dissemination of disaster information on X and provides insights for both academia and emergency management practitioners in using CMC platforms

Viherjihadismia ja vastuun huhuilua : huhujen käytöstä verkkokeskusteluissa

Tässä työssä tarkastellaan huhuja verkkokeskusteluissa. Huhuista tämän työn keskiöön on nostettu kaksi tapaustutkimusta. Toinen huhuista koskee kotitalousjätteiden kierrättämisen oletettua turhuutta ja toinen maahanmuuttajien sosiaalituen suuruutta. Työn tavoite on vastata seuraaviin kysymyksiin: Mikä on huhujen sisältö tämän tutkielman aineistossa ja mitä näitä huhuja levittämällä argumentoidaan? Tunnistetaanko huhupuhe genrenä ja puhetyylinä keskustelijoiden joukossa? Kuinka huhupuheeseen suhtaudutaan ja miten genren nimeämistä käytetään retorisena välineenä? Minkälaisia motiiveja huhun kommentoimiselle ilmenee? Lopuksi selvitetään minkälaisia stereotyyppisiä kuvauksia huhut rakentavat kohteistaan? Minkälaisia nämä tapaustutkimuksiin kytkeytyvät kuvaukset ovat muualla aineistossa, tapaustutkimuksia laajemmassa kontekstissa? Tapaustutkimusten aineistona toimivat keskustelut käydään pääosin aiheisiin liittyvien verkkouutisten alla, mutta niitä kommentoidaan myös muilla verkkoalustoilla. Tapaustutkimuksissa ilmenevät stereotyyppiset kuvaukset eivät ole yleisiä vain tämän työn tapaustutkimusten parissa, vaan ne kytkeytyvät laajemmin aineistossa esiintyviin diskursseihin. Näin ollen aineisto on laajempi ja sitä rajaavat tapaustutkimusten yhteydessä esiin nousseet diskurssit. Aineistosta nousee esiin kaksi huhua: 1) Maahanmuuttajilla on kalliimmat lastenvaunut, koska he saavat paljon suurempaa sosiaalitukea kuin suomalaiset. 2) Jätteiden syntypaikkalajittelu on turhaa, koska jätteet sekoitetaan myöhemmässä vaiheessa prosessia takaisin yhteen. Lähilukemisen, diskurssianalyysin ja argumentaatioanalyysin avulla aineistosta ilmenee, että huhuja levittämällä ja niitä kommentoimalla argumentoidaan eri motiivein monenlaisia yhteiskunnallisia asioita. Genren nimeämistä käytetään eri tavoin retorisena välineenä, useimmiten pyrkien heikentämään huhupuheiden sisältämien väitteiden totuusarvoa. Huhupuheet tunnistetaan aineistossa melko hyvin, mutta niiden tarkempi nimeäminen vaihtelee. Tapaustutkimuksista nousee esiin joitakin selkeitä stereotyyppisiä kuvauksia, kuten vaatimattomat suomalaiset, ahneet maahanmuuttajat ja selvemmin poliittisena konstruktiona epärationaalinen vihervasemmisto. Nämä stereotyyppiset kuvaukset kytkeytyvät laajempaan yhteiskunnalliseen verkkokeskusteluaineistoon ja niitä konstruoidaan monipuolisesti erilaisia nimityksiä käyttämällä ja ajallisesti laajalti eri aikoina, monissa eri yhteyksissä. Tämä työ osoittaa, kuinka kansanperinteen genret toimivat keskustelussa asenteiden ilmaisijoina ja perinteistä tietoa voidaan käyttää sosiaalisen todellisuuden muokkaajana sekä vakuuttelun keinona

Towards Evaluating Veracity of Textual Statements on the Web

The quality of digital information on the web has been disquieting due to the absence of careful checking. Consequently, a large volume of false textual information is being produced and disseminated with misstatements of facts. The potential negative influence on the public, especially in time-sensitive emergencies, is a growing concern. This concern has motivated this thesis to deal with the problem of veracity evaluation. In this thesis, we set out to develop machine learning models for the veracity evaluation of textual claims based on stance and user engagements. Such evaluation is achieved from three aspects: news stance detection engaged user replies in social media and the engagement dynamics. First of all, we study stance detection in the context of online news articles where a claim is predicted to be true if it is supported by the evidential articles. We propose to manifest a hierarchical structure among stance classes: the high-level aims at identifying relatedness, while the low-level aims at classifying, those identified as related, into the other three classes, i.e., agree, disagree, and discuss. This model disentangles the semantic difference of related/unrelated and the other three stances and helps address the class imbalance problem. Beyond news articles, user replies on social media platforms also contain stances and can infer claim veracity. Claims and user replies in social media are usually short and can be ambiguous; to deal with semantic ambiguity, we design a deep latent variable model with a latent distribution to allow multimodal semantic distribution. Also, marginalizing the latent distribution enables the model to be more robust in relatively smalls-sized datasets. Thirdly, we extend the above content-based models by tracking the dynamics of user engagement in misinformation propagation. To capture these dynamics, we formulate user engagements as a dynamic graph and extract its temporal evolution patterns and geometric features based on an attention-modified Temporal Point Process. This allows to forecast the cumulative number of engaged users and can be useful in assessing the threat level of an individual piece of misinformation. The ability to evaluate veracity and forecast the scale growth of engagement networks serves to practically assist the minimization of online false information’s negative impacts

Crowd and AI Powered Manipulation: Characterization and Detection

User reviews are ubiquitous. They power online review aggregators that influence our daily-based decisions, from what products to purchase (e.g., Amazon), movies to view (e.g., Netflix, HBO, Hulu), restaurants to patronize (e.g., Yelp), and hotels to book (e.g., TripAdvisor, Airbnb). In addition, policy makers rely on online commenting platforms like Regulations.gov and FCC.gov as a means for citizens to voice their opinions about public policy issues. However, showcasing the opinions of fellow users has a dark side as these reviews and comments are vulnerable to manipulation. And as advances in AI continue, fake reviews generated by AI agents rather than users pose even more scalable and dangerous manipulation attacks. These attacks on online discourse can sway ratings of products, manipulate opinions and perceived support of key issues, and degrade our trust in online platforms. Previous efforts have mainly focused on highly visible anomaly behaviors captured by statistical modeling or clustering algorithms. While detection of such anomalous behaviors helps to improve the reliability of online interactions, it misses subtle and difficult-to-detect behaviors. This research investigates two major research thrusts centered around manipulation strategies. In the first thrust, we study crowd-based manipulation strategies wherein crowds of paid workers organize to spread fake reviews. In the second thrust, we explore AI-based manipulation strategies, where crowd workers are replaced by scalable, and potentially undetectable generative models of fake reviews. In particular, one of the key aspects of this work is to address the research gap in previous efforts for anomaly detection where ground truth data is missing (and hence, evaluation can be challenging). In addition, this work studies the capabilities and impact of model-based attacks as the next generation of online threats. We propose inter-related methods for collecting evidence of these attacks, and create new countermeasures for defending against them. The performance of proposed methods are compared against other state-of-the-art approaches in the literature. We find that although crowd campaigns do not show obvious anomaly behavior, they can be detected given a careful formulation of their behaviors. And, although model-generated fake reviews may appear on the surface to be legitimate, we find that they do not completely mimic the underlying distribution of human-written reviews, so we can leverage this signal to detect them