Social influence analysis in microblogging platforms - a topic-sensitive based approach

The use of Social Media, particularly microblogging platforms such as Twitter, has proven to be an effective channel for promoting ideas to online audiences. In a world where information can bias public opinion it is essential to analyse the propagation and influence of information in large-scale networks. Recent research studying social media data to rank users by topical relevance have largely focused on the “retweet", “following" and “mention" relations. In this paper we propose the use of semantic profiles for deriving influential users based on the retweet subgraph of the Twitter graph. We introduce a variation of the PageRank algorithm for analysing users’ topical and entity influence based on the topical/entity relevance of a retweet relation. Experimental results show that our approach outperforms related algorithms including HITS, InDegree and Topic-Sensitive PageRank. We also introduce VisInfluence, a visualisation platform for presenting top influential users based on a topical query need

Factors Affecting Retweetability: An Event-Centric Analysis on Twitter

In Twitter information primarily propagates through retweet mechanism. While a massive amount of tweets gets generated everyday, only a handful of them get retweeted widely. In this study, we have investigated the impact of user-roles in retweet phenomena. We have introduced the concept of “Information Diffusion Impact” (IDI) and identified three important user roles, namely “information starter”, “amplifier”, and “transmitter”. Retweetability has been modeled using IDI impact for different user roles along with the content features like presence of hashtag, URL etc. Further, the effect of a major event on the factors affecting retweetability has been investigated. Our findings demonstrate that retweetability is significantly affected by amplifiers and information-starters and these effects change substantially due to event. We have also reexamined our model in another dataset of the Boston marathon bomb blast, 2013 and the outcome of this analysis is in good agreement with our findings from Japan earthquake dataset

A Visualized and Bibliometric Analysis of Information-related Research on COVID-19

When a public health crisis occurs, people’s needs for information increase sharply and information access can be a matter of life and death. To understand citizens’ information-related behavior under COVID-19, information researchers published prolifically. The present study aims to map the contour of COVID-19 researches relating to information. Publications relating to information issues during COVID-19 pandemic were retrieved from the Web of Science Core Collection. Using the Citespace bibliometric tool, most productive authors, journals, institutions, countries and most cited articles were identified. Keyword co-occurrence and cluster analysis were conducted to reveal dominant topics and research trends. The 511 articles meeting the filter criteria were published by authors from a total of 66 countries. The United States contributed 190 articles, ranking first globally. Dominant topics included the role of technology, crisis communication, COVID-19 information management, information literacy and misinformation on social media. But scant attention was directed to the role of individuals situated in the middle of information flows, to the informational relevance of personal narratives circulating through social media and to country- or disaster-based comparative studies. Researchers can also observe whether COVID-19-driven informational interventions continue as standard practice after the pandemic ends

Yhteisöllinen tiedonrakentelu ja verkottunut asiantuntijuus Twitter-palvelussa : Case #okfest

Aims. This qualitative study explored a phenomenon of epistemic communality around a Twitter hashtag. The primary aim of the study was to explore communal epistemic production on the Twitter platform, especially in the context of a mutually shared hashtag. The study explored the peer-production of knowledge and epistemic structures in the context of a specialist domain collaborating in the open Web. The secondary aim was to explore how Twitter functions as a platform for networked expertise and as a public agora for practitioners' expert discourse. This nascent mode of cultural production leads to the development of expert cultures on Twitter and in the open Web. This creates new contexts for informal collaborative learning and cultral production potentially answering some of the competence challenges presented by the 21st century. Methods. The hashtag #okfest was launched for the 'Open Knowledge Festival' conference held in Helsinki, Finland (17–22.9.2012). The participants of the study were open knowledge practitioners who participated in the hashtag discourse of #okfest on Twitter. All public tweets containing the string '#okfest' were collected as data. Tweets were analyzed with qualitative thematic analysis exploring the epistemic contributions either included in the tweets or as hyperlinked attachments. Results and conclusions. The analysis indicated how the hashtag was appropriated to serve as a node of communal knowledge sharing beyond mere reporting from the conference. The analysis observed six themes of communal knowledge building in the hashtag space. The communal epistemic activities in #okfest were likened to the properties of a community of practice (Wenger, 1998). A network of practitioners engaging in a mutual domain creates a dynamic 'social learning system' combining social interaction with the production and dissemination of knowledge. The study yielded a novel theoretical concept of 'expert microblogging', recognized as a significant genre of cultural production in a specialist domain on Twitter and in the open Web. Finally the Twitter platform was ascertained as a site for the manifestation of cultures of networked expertise.Tavoitteet. Tämä laadullinen tutkielma tutki episteemistä yhteisöllisyyttä Twitter-palvelussa hashtag-aihetunnisteen ympärillä. Hashtag #okfest lanseerattiin Helsingissä pidetyn 'Open Knowledge Festival' –konferenssin taustakanavaksi 17–22.9.2012. Tutkielman pääasiallinen tavoite oli tutkia yhteisöllistä tiedonrakentelua Twitter-palvelussa erityisesti hashtagien ympärillä. Tutkimus kohdistui tietyn toimialan tiedolliseen vertaistuotantoon Twitterissä ja avoimessa Internetissä. Laajempi tavoite oli tutkia miten Twitter toimii alustana verkottuneelle asiantuntijuudelle ja julkisten asiantuntijayhteisöjen vuorovaikutukselle. Tämä uusi kulttuurisen tuotannon konteksti mahdollistaa verkottuneiden asiantuntijakulttuurien kehittymisen Twitterissä ja avoimessa Internetissä. Tämä luo uusia tilaisuuksia informaalille yhteisölliselle oppimiselle ja kulttuuriselle tuotannolle mahdollisesti vastaten nykyajan vaativiin osaamishaasteisiin. Menetelmät. Tutkimuksen osallistujat olivat avoimen datan ammattilaisia, jotka osallistuivat Twitterissä #okfest keskusteluun konferenssin aikana. Kaikki julkiset Twitter-viestit #okfest aihetunnisteella kerättiin aineistoksi. Viestejä analysoitiin laadullisella temaattisella analyysillä koskien niiden tiedollisia kontribuutioita joko viestiin sisältyen tai linkitettynä. Tulokset ja johtopäätökset. Tutkimustulokset osoittavat että hashtag-aihetunnisteen ympärille syntyi yhteisöllisen tiedonrakentelun ilmiö, joka oli enemmän kuin pelkkää raportointia tapahtumapaikalta. Analyysissä löytyi kuusi yhteisöllisen tiedonrakentelun teemaa jotka ilmenivät hashtag-tilassa. Yhteisöllinen tiedonrakentelu muistutti käytäntöyhteisöjen teoriaperinteen (Wenger, 1998) vuorovaikutuksen piirteitä. Asiantuntijoiden yhteisöllinen vuorovaikutus synnytti "sosiaalisen oppimisen systeemin" jossa tiedonrakentelu yhdistyi vuorovaikutukseen. Tutkimustuloksista nousi uusi käsitteellistys, asiantuntijoiden alakohtainen tiedollinen tuotanto (eng. expert microblogging). Twitter-alustalle paikantui verkottuneiden asiantuntijakulttuurien kehittyminen avoimessa verkossa

Computing tie strength

Relationships make social media social. But, not all relationships are created equal. We have colleagues with whom we correspond intensely, but not deeply; we have childhood friends we consider close, even if we fell out of touch. Social media, however, treats everybody the same: someone is either a completely trusted friend or a total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the name tie strength, a term for the strength of a relationship between two people. Despite many compelling findings along this line of research, social media does not incorporate tie strength or its lessons. Neither does most research on large-scale social phenomena. In social network analyses, a link either exists or not. Relationships have few properties of their own. Simply put, we do not understand a basic property of relationships expressed online. This dissertation addresses this problem, merging the theories behind tie strength with the data from social media. I show how to reconstruct tie strength from digital traces in online social media, and how to apply it as a tool in design and analysis. Specifically, this dissertation makes three contributions. First, it offers a rich, high-accuracy and general way to reconstruct tie strength from digital traces, traces like recency and a message???s emotional content. For example, the model can split users into strong and weak ties with nearly 89% accuracy. I argue that it also offers us a chance to rethink many of social media???s most fundamental design elements. Next, I showcase an example of how we can redesign social media using tie strength: a Twitter application open to anyone on the internet which puts tie strength at the heart of its design. Through this application, called We Meddle, I show that the tie strength model generalizes to a new online community, and that it can solve real people???s practical problems with social media. Finally, I demonstrate that modeling tie strength is an important new tool for analyzing large-scale social phenomena. Specifically, I show that real-life diffusion in online networks depends on tie strength (i.e., it depends on social relationships). As a body of work, diffusion studies make a big simplifying assumption: simple stochastic rules govern person-to-person transmission. How does a disease spread? With constant probability. How does a chain letter diffuse? As a branching process. I present a case where this simplifying assumption does not hold. The results challenge the macroscopic diffusion properties in today???s literature, and they hint at a nest of complexity below a placid stochastic surface. It may be fair to see this dissertation as linking the online to the offline; that is, it connects the traces we leave in social media to how we feel about relationships in real life

RELISON: A Framework for Link Recommendation in Social Networks

Link recommendation is an important and compelling problem at the intersection of recommender systems and online social networks. Given a user, link recommenders identify people in the platform the user might be interested in interacting with. We present RELISON, an extensible framework for running link recommendation experiments. The library provides a wide range of algorithms, along with tools for evaluating the produced recommendations. RELISON includes algorithms and metrics that consider the potential effect of recommendations on the properties of online social networks. For this reason, the library also implements network structure analysis metrics, community detection algorithms, and network diffusion simulation functionalities. The library code and documentation is available at https://github.com/ir-uam/RELISON