Characterizing silent users in social media communities

Silent users often constitute a significant proportion of an online user-generated content system. In the context of social media such as Twitter, users can opt to be silent all or most of the time. They are often called the invisible participants or lurkers. As lurkers contribute little to the online content, existing analysis often overlooks their presence and voices. However, we argue that understanding lurkers is important in many applications such as recommender systems, targeted advertising, and social sensing. This research therefore seeks to characterize lurkers in social media and propose methods to profile them. We examine 18 weeks of tweets generated by two Twitter communities consisting of more than 110K and 114K users respectively. We find that there are many lurkers in the two communities, and the proportion of lurkers in each community changes with time.We also show that by leveraging lurkers' neighbor content, we are able to profile them with accuracy comparable to that of profiling active users. It suggests that user generated content can be utilized for profiling lurkers and lurkers in Twitter are after all not that ``invisible''

SEIZ Matters: Modelling the spread of concepts on Twitter

How do different concepts spread on social media? This question is becoming increasingly important as much of our time, discussion, and news consumption move online. This paper investigates the use of two models from epidemiology, namely the classical SI-model and the sociology-inspired SEIZ-model, to model and understand this phenomenon. I study the spread of two concepts during the 2019 Danish national election, klimatosse (climate fool) and Paludan on Twitter, both of which played key roles in the election season and had epidemic qualities in their usage throughout social media.  I find that although both models can provide decent fits of the data, the SEIZ-model outperforms the SI-model by a wide margin. Furthermore, the parameters can be interpreted to provide a deeper understanding of the two phenomena and how they spread.How do different concepts spread on social media? This question is becoming increasingly important as much of our time, discussion, and news consumption move online. This paper investigates the use of two models from epidemiology, namely the classical SI-model and the sociology-inspired SEIZ-model, to model and understand this phenomenon. I study the spread of two concepts during the 2019 Danish national election, klimatosse (climate fool) and Paludan on Twitter, both of which played key roles in the election season and had epidemic qualities in their usage throughout social media.  I find that although both models can provide decent fits of the data, the SEIZ-model outperforms the SI-model by a wide margin. Furthermore, the parameters can be interpreted to provide a deeper understanding of the two phenomena and how they spread

Are Online Parasites Really Different from Lurkers?

With the development of digital technology, the internet environment has dramatically changed the way people share information, which has been changed by different types of sources, making it convenient to obtain information. The lurking phenomenon in the network is becoming increasingly common, and previous studies have been conducted on lurkers on the internet with shifting focus from active users to passive users. Under these circumstances, this tries to conceptualize a new type of passive users, titled as “online parasites” who focus on obtaining information by utilizing the internet or their host to achieve their other purposes. The aim is to deeply understand these users and clearly distinguish them from other types of users such as lurkers

Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media

To what extent user's stance towards a given topic could be inferred? Most of the studies on stance detection have focused on analysing user's posts on a given topic to predict the stance. However, the stance in social media can be inferred from a mixture of signals that might reflect user's beliefs including posts and online interactions. This paper examines various online features of users to detect their stance towards different topics. We compare multiple set of features, including on-topic content, network interactions, user's preferences, and online network connections. Our objective is to understand the online signals that can reveal the users' stance. Experimentation is applied on tweets dataset from the SemEval stance detection task, which covers five topics. Results show that stance of a user can be detected with multiple signals of user's online activity, including their posts on the topic, the network they interact with or follow, the websites they visit, and the content they like. The performance of the stance modelling using different network features are comparable with the state-of-the-art reported model that used textual content only. In addition, combining network and content features leads to the highest reported performance to date on the SemEval dataset with F-measure of 72.49%. We further present an extensive analysis to show how these different set of features can reveal stance. Our findings have distinct privacy implications, where they highlight that stance is strongly embedded in user's online social network that, in principle, individuals can be profiled from their interactions and connections even when they do not post about the topic.Comment: Accepted as a full paper at CSCW 2019. Please cite the CSCW versio

Bias-Aware Design for Informed Decisions: Raising Awareness of Self-Selection Bias in User Ratings and Reviews

People often take user ratings and reviews into consideration when shopping for products or services online. However, such user-generated data contains self-selection bias that could affect people decisions and it is hard to resolve this issue completely by algorithms. In this work, we propose to raise the awareness of the self-selection bias by making three types of information concerning user ratings and reviews transparent. We distill these three pieces of information (reviewers experience, the extremity of emotion, and reported aspects) from the definition of self-selection bias and exploration of related literature. We further conduct an online survey to assess the perceptions of the usefulness of such information and identify the exact facets people care about in their decision process. Then, we propose a visual design to make such details behind user reviews transparent and integrate the design into an experimental website for evaluation. The results of a between-subjects study demonstrate that our bias-aware design significantly increases the awareness of bias and their satisfaction with decision-making. We further offer a series of design implications for improving information transparency and awareness of bias in user-generated content

MOTIVATIONS BEHIND CUSTOMER ENGAGEMENT BEHAVIOR ON SOCIAL MEDIA : The mediating role of culture

VAASAN YLIOPISTO Markkinoinnin ja viestinnän yksikkö Tekijä: Jemina Berglund Tutkielman nimi: Motivations Behind Customer Engagement Behavior On Social Media. The mediating role of culture. Tutkinto: Kauppatieteiden maisteri Oppiaine: Kansainvälinen liiketoiminta Työn ohjaaja: Minnie Kontkanen Valmistumisvuosi: 2020 Sivumäärä: 83 TIIVISTELMÄ: Yritysten tavoitteena on sitouttaa kuluttajia ja saada heitä tykkäämään, jakamaan, kommentoimaan ja tuottamaan yritykseen liittyvää sisältöä sosiaalisessa mediassa. Vaikka aikaisemmassa kirjallisuudessa on tutkittu käyttäjien motiiveja syventää suhdettaan yrityksiin, on se silti vielä hajanaista ja sidottua tiettyyn käytökseen tai kontekstiin, kuten brändiyhteisöihin. Lisäksi yksilön kulttuuritaustan on esitetty mahdollisesti vaikuttavan käyttäytymiseen sosiaalisessa mediassa, mutta kulttuurin vaikutuksesta on vain vähän tutkimusta. Aikaisempi tutkimus kulttuurin vaikutuksista on hajanaista ja käyttää usein kansallisen tason dimensioita mittaamaan yksilötason käytöstä. Lisäksi tutkimuksissa on esiintynyt erilaisia tuloksia sen suhteen mitkä dimensiot vaikuttavat käyttäytymiseen ja motiiveihin sosiaalisessa mediassa. Tämä tutkimus pyrkii lisäämään ymmärrystä siitä mitkä motiivit vaikuttavat kuluttajan käyttäytymiseen sosiaalisessa mediassa etenkin brändeihin liittyen ja millaisia eri käytöksiä linkittyy kuhunkin motiiviin. Lisäksi yksilön kulttuuritaustaa ymmärtämällä pyritään analysoimaan mitkä dimensiot voisivat vaikuttaa käyttäytymiseen. Tutkimukseen valittiin vastaajia kahdesta eri kulttuuritaustasta, Turkista sekä Tanskasta, sillä nämä maat eroavat individualismi dimensiolla, joka on aikaisemmin linkitetty eroavaisuuksiin online käytöksessä. Tietoa motiiveista ja köytöksestä kerättiin puolistrukturoidulla haastattelumenetelmällä. Lisäksi vastaajia pyydettiin täyttämään kysely, jolla on aikaisemmassa tutkimuksessa mitattu kulttuuridimensioita yksilötasolla kansallisen tason sijaan. Kulttuurisia arvoja käytettiin taustatietona haastattelumateriaalin analysoinnissa. Aiempien tutkimusten kanssa yhtenäistä oli vastaajien halu löytää informaatiota ja inspiraatiota sosiaalisesta mediasta ja etenkin Instagramista. Lisäksi yleinen sosiaalisen median käytön motiivi oli pitää yhteyttä ystäviin ja jakaa sisältöä kuten kuvia tai tarinoita omassa profiilissa. Aikaisempi tutkimus on myös ehdottanut, että itseilmaisu ja omien mielipiteiden esiin tuominen olisivat keskeisiä motiiveja käytökselle. Kuitenkaan tässä tutkimuksessa, vastaajat eivät pitäneet itseilmaisua tärkeänä motiivina brändeihin liittyen. Tämä tutkimus osoittaakin käyttäjien olevan suhteellisen passiivisia brändejä kohtaan ja usein käytös rajoittuukin vain sisällön seuraamiseen eikä niinkään tuottamiseen, vaikka jotkut käyttäjistä ilmaisivatkin olevansa inspiroituneita yritysten tuottamasta sisällöstä ja sen vaikutuksesta heidän ostokäyttäytymiseensä. Kulttuurin vaikutuksia ei voitu suoraan päätellä tutkimuksen luonteen takia. Muutama vastaaja, joilla oli korkea individualismi, kertoivat seuraavansa vain uniikimpia brändejä, ehkä viestiäkseen erilaisuutta. Kuitenkin lisää tutkimusta tarvitaan määrittämään kuinka kulttuuri vaikuttaa motiiveihin ja käytökseen. Tämän tutkimuksen tulokset auttavat yrityksiä luomaan sosiaalisen median strategiaa, joka houkuttelee käyttäjiä syventämään suhdettaan yritykseen. _______________________________________________________________________ AVAINSANAT: Customer engagement behavior, Social media engagement, Social media marketing, Motivations, Culture

Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media

ABSTRACT Millions of people regularly report the details of their realworld experiences on social media. This provides an opportunity to observe the outcomes of common and critical situations. Identifying and quantifying these outcomes may provide better decision-support and goal-achievement for individuals, and help policy-makers and scientists better understand important societal phenomena. We address several open questions about using social media data for open-domain outcome identification: Are the words people are more likely to use after some experience relevant to this experience? How well do these words cover the breadth of outcomes likely to occur for an experience? What kinds of outcomes are discovered? Studying 3-months of Twitter data capturing people who experienced 39 distinct situations across a variety of domains, we find that these outcomes are generally found to be relevant (55-100% on average) and that causally related concepts are more likely to be discovered than conceptual or semantically related concepts