38 research outputs found

    On the use of multi-sensor digital traces to discover spatio-temporal human behavioral patterns

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    134 p.La tecnología ya es parte de nuestras vidas y cada vez que interactuamos con ella, ya sea en una llamada telefónica, al realizar un pago con tarjeta de crédito o nuestra actividad en redes sociales, se almacenan trazas digitales. En esta tesis nos interesan aquellas trazas digitales que también registran la geolocalización de las personas al momento de realizar sus actividades diarias. Esta información nos permite conocer cómo las personas interactúan con la ciudad, algo muy valioso en planificación urbana,gestión de tráfico, políticas publicas e incluso para tomar acciones preventivas frente a desastres naturales.Esta tesis tiene por objetivo estudiar patrones de comportamiento humano a partir de trazas digitales. Para ello se utilizan tres conjuntos de datos masivos que registran la actividad de usuarios anonimizados en cuanto a llamados telefónicos, compras en tarjetas de crédito y actividad en redes sociales (check-ins,imágenes, comentarios y tweets). Se propone una metodología que permite extraer patrones de comportamiento humano usando modelos de semántica latente, Latent Dirichlet Allocation y DynamicTopis Models. El primero para detectar patrones espaciales y el segundo para detectar patrones espaciotemporales. Adicionalmente, se propone un conjunto de métricas para contar con un métodoobjetivo de evaluación de patrones obtenidos

    Information Communication Technology for Crisis Management and Shared Situational Awareness: Social Media Public Health Communication During the COVID-19 Pandemic

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    This comprehensive study analyzes the role of social media, specifically Facebook, in crisis communication during the COVID-19 pandemic. Conducted through a longitudinal netnography approach, the research scrutinizes the communication strategies of Australian public health agencies from 2019 to 2020 and their impact on shared situational awareness (SSA). Drawing on the Seppänen et al. (2013) model for crisis communication, the study looks at three key aspects - link content (information), link type (communication), and link quality (trust) - and evaluates how they influence SSA during a crisis. The application of Chaos Theory further enhances the research's depth by identifying patterns and transformations in Facebook communications before and after the COVID-19 disruption. The study reveals that the pandemic significantly disrupted typical health communication strategies, leading to new emergent patterns, and it also underscores the critical role of secondary communication and emotional factors in the public's decision to share crisis information. Moreover, it identifies issues such as misinformation and inconsistency in messaging as significant obstacles to the public's trust in official health communications, ultimately impeding the creation of adequate SSA. The research emphasizes the need to develop consistent, clear, and reliable messaging strategies for effective crisis communication. The findings expand existing knowledge on social media utilization in crisis communication, offering valuable insights to enhance public health agencies' communication strategies, thereby aiding in the creation of trusted SSA. Unlike prior studies focusing on crisis response teams, this research concentrates on shared situational awareness among the general public, providing practical recommendations to improve social media crisis communication for more effective response and management in a health crisis

    Dialogic Language as Digital Ethos: an Analysis of Language Used in the Anti-Vaccine Conversation on Twitter

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    Many scholars attribute social media’s influence with a rise in distrust of expert advice. These scholars have suggested that people are turning to non-experts for advice because those non-experts seem to be more willing to openly discuss medical issues while also providing empathy, as opposed to the experts who have been trained to speak with detached authority. For this dissertation, I have done a study to find evidence supporting these theories. To do this, I looked at the Twitter conversation which has been focusing on anti-vaccination themes. Drawing on tweets from within that conversation, I conducted an inter-rater reliability test to categorize 1,000 tweets as either using a more empathetic and conversational tone versus those with the authoritative tone traditionally favored by experts. I then used those evaluations to conduct machine learning to evaluate over 50,000 additional tweets from the anti-vaccination conversation. I evaluated the relative success of tweets those tweets which used “authoritative” language compared to those that used “dialogic” language. Through this research, I was able to find a correlation between the degree to which the language within a tweet seemed to express empathy and encourage give-and-take forms of conversation and with engagement rates achieved by those tweets. Analysis suggests that the amount of influence this language use has on engagement rates is relatively minor, with tweets using stronger levels of dialogic language earning approximately one additional like for every 5,000 followers an account may have over tweets using primarily authoritative language. This study was done with the intention of considering how an audience’s preference for dialogic language might influence the way we prioritize authoritative voice in academic writing. As the data only marginally confirms this preference, this study shifts focus to ways of teaching students to be more responsible as readers in lieu of relying on experts using a more empathetic voice

    Ranking Influential Nodes of Fake News Spreading on Mobile Social Networks

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    Online fake news can generate a negative impact on both users and society. Due to the concerns with spread of fake news and misinformation, assessing the network influence of online users has become an important issue. This study quantifies the influence of nodes by proposing an algorithm based on information entropy theory. Dynamic process of influence of nodes is characterized on mobile social networks (MSNs). Weibo (i.e., the Chinese version of microblogging) users are chosen to build the real network and quantified influence of them is analyzed according to the model proposed in this paper. MATLAB is employed to simulate and validate the model. Results show the comprehensive influence of nodes increases with the rise of two factors: the number of nodes connected to them and the frequency of their interaction. Indirect influence of nodes becomes stronger than direct influence when the network scope rises. This study can help relevant organizations effectively oversee the spread of online fake news on MSNs

    Characterization of behavioral patterns exploiting description of geographical areas

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    Abstract The enormous amount of recently available mobile phone data is providing unprecedented direct measurements of human behavior. Early recognition and prediction of behavioral patterns are of great importance in many societal applications like urban planning, transportation optimization, and health-care. Understanding the relationships between human behaviors and location's context is an emerging interest for understanding human-environmental dynamics. Growing availability of Web 2.0, i.e. the increasing amount of websites with mainly user created content and social platforms opens up an opportunity to study such location's contexts. This paper investigates relationships existing between human behavior and location context, by analyzing log mobile phone data records. First an advanced approach to categorize areas in a city based on the presence and distribution of categories of human activity (e.g., eating, working, and shopping) found across the areas, is proposed. The proposed classification is then evaluated through its comparison with the patterns of temporal variation of mobile phone activity and applying machine learning techniques to predict a timeline type of communication activity in a given location based on the knowledge of the obtained category vs. land-use type of the locations areas. The proposed classification turns out to 1 arXiv:1510.02995v1 [cs.SI] 11 Oct 2015 be more consistent with the temporal variation of human communication activity, being a better predictor for those compared to the official land use classification

    Modelling fashion microblogs to increase the influence of social media marketing in Ireland and China

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    With the breakthrough of social media in the 21st century, microblogging has become an influential medium for marketing fashion brands and products online. For this reason, this study explores ten Irish and another ten Chinese fashion microblogging influencers’ microblogs using Text Mining and Netnography. By this comparison, the study finds a current model of how fashion microblogs influence fashion consumption in Ireland and China. With the help of this model, the study proposes a typology of Irish and Chinese fashion microblogging influencers and their basic microblogging strategies. The proposed typology intends to help fashion marketers to model their fashion microblogs in order to increase the influence of social media marketing in Ireland and China. Furthermore, the proposed typology is applied to develop a digital artefact that not only can deal with Irish and Chinese fashion microblogs at the same time but also show the results employing text visualisation. This bilingual digital website tries to make up for the lack of attention to text analysis on fashion-related words in the development of text mining tools. Finally, the methodological combination of Text Mining and Netnography employs digital tools and computer programming to conduct studies in the field of arts and humanities. The success of methodological combination in the study opens up a bright prospect for interdisciplinary research methodology

    The role of geographic knowledge in sub-city level geolocation algorithms

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    Geolocation of microblog messages has been largely investigated in the lit- erature. Many solutions have been proposed that achieve good results at the city-level. Existing approaches are mainly data-driven (i.e., they rely on a training phase). However, the development of algorithms for geolocation at sub-city level is still an open problem also due to the absence of good training datasets. In this thesis, we investigate the role that external geographic know- ledge can play in geolocation approaches. We show how di)erent geographical data sources can be combined with a semantic layer to achieve reasonably accurate sub-city level geolocation. Moreover, we propose a knowledge-based method, called Sherloc, to accurately geolocate messages at sub-city level, by exploiting the presence in the message of toponyms possibly referring to the speci*c places in the target geographical area. Sherloc exploits the semantics associated with toponyms contained in gazetteers and embeds them into a metric space that captures the semantic distance among them. This allows toponyms to be represented as points and indexed by a spatial access method, allowing us to identify the semantically closest terms to a microblog message, that also form a cluster with respect to their spatial locations. In contrast to state-of-the-art methods, Sherloc requires no prior training, it is not limited to geolocating on a *xed spatial grid and it experimentally demonstrated its ability to infer the location at sub-city level with higher accuracy

    Investor Sentiment and Attention in Capital Markets - A (Social) Media Perspective

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    This dissertation examines the impact of social and traditional media on capital markets. The empirical tests focus on investor sentiment which, for example, can be captured by postings on social media platforms, innovative news databases and the textual analysis of traditional media press. The research direction of this dissertation implicitly questions the assumptions stated by the traditional finance theory. Our new empirical findings and their explanations are, hence, closely linked with the behavioral finance theory. The Efficient Market Hypothesis constitutes one of the fundamental pillars of the traditional finance theory. In this concept, the availability of information is the basic requirement for the functionality of efficient capital markets. New information is quickly and correctly incorporated into an asset’s price. The new price of an asset, therefore, immediately reflects the updated fundamental value (Fama, 1969; 1970). However, various studies have recently shown that stock market movements are not always associated with rational information about an asset’s value. The observation of over- and underreaction of asset prices to news signals or distinctive return patterns gave reason for the gaining importance of the behavioral finance theory since the 1990’s. The changing availability and the easier access to information for institutional and individual investors play an important role in this recent development. For example, Figure 1 1 (p. 3) depicts the circulation of US newspapers between 1970 and 2017. The number of households covered by traditional media press decreased from more than 60 million to around 30 million households in 2017. The establishment of the internet, on the other hand, parallelly accelerated the digital development in the media landscape. Figure 1 3 (p. 5) describes the global development of social media users since 2010. The number of social media users is expected to increase from 1 billion users in 2010 to around 3 billion users in 2021. This development not only affects the society but also a specific focus group of this dissertation: the financial investors. The way investors gather, process, and disseminate information also experienced a significant change in recent decades (Puppis et al., 2017). In this connection, the development of investor attention and sentiment for individual assets is sustainably impacted by the digitalization of media channels. Consequently, we derive four fundamental research questions, which accompany the empirical analyses of this dissertation: 1. What role does investor sentiment play in financial markets? Do investors solely follow the market, or do beliefs of investors predict future returns or other market variables? 2. How does (social) media relate to financial markets in the general daily context and specifically around news events, such as earnings or M&A announcements? 3. What kind of firms are more sensitive to investor sentiment than others? 4. Does arbitrage stabilize financial markets against noise traders? The following structure of this dissertation aims to answer these questions in the best possible way: The first chapter introduces the reader to the relevance of the topic and the leading research questions of the dissertation. The second chapter lays the theoretical foundation and describes the fundamental concepts of the traditional and also the behavioral finance theory, which aims to comprehensively explain selected market anomalies. Also, we summarize selected psychological concepts that help to explain irrational actions of investors, which potentially cause market volatility and asset prices to deviate from their fundamental value. Literature reviews on investor sentiment in close relationship with traditional and social media complete the second chapter. The third chapter encompasses the first empirical work of this dissertation and primarily explores the impact of social media on capital markets. The empirical analysis falls back to more than 4.5 million posts on the leading Australian financial internet message board HotCopper between January 2008 and May 2016. The findings suggest that social media activity is price relevant for capital markets. Positive investor sentiment, for example, is in this connection contemporaneously and significantly correlated with a stock’s abnormal return. However, the effect diminishes after one month. Arbitrage of presumably informed investors only partially countervail this effect. Postings by individual investors on social media, hence, cause capital markets to overreact to potentially non-relevant information in the short-term. However, negative investor sentiment expressed in internet message boards provides a differentiated picture. Negative investor sentiment is significantly related with the next month’s abnormal returns. Also, an increasing rate of agreement on negative investor sentiment before earnings announcements forecasts negative earnings surprises. Both findings support the information hypothesis that negative internet message board postings contain value-relevant information. The question whether social media activity induces market volatility remains ambiguous. The Granger-tests and the reactions of the impulse-response functions show a bilateral relationship between return volatility and the number of internet message board postings. However, we find in this context that individual investors react more sensitive to market volatility on social media than the other way around. In summary, the results of the first empirical work provide evidence for the economic significance of investor sentiment measured on social media and its asymmetric role in capital markets. We extend the empirical analysis in the fourth chapter of this dissertation and investigate the impact of traditional and social media on target price run-ups before bid announcements. The literature previously documented an increase in the target stock price two months prior to the official bid announcement (e.g., Keown and Pinkerton, 1981). This phenomenon is also referred to as the target run-up. One group of researchers find explanations within the insider hypothesis (leakage of insider information prior to the bid announcement). Another group argues based on the market expectation hypothesis (the market anticipates publicly available information to predict upcoming mergers). Our second empirical work considers 2,765 bid announcements in Australia between January 2008 and August 2015. We use more than 15 thousand news articles, more than 80 thousand posts on the internet message board HotCopper, analyst recommendations, and Google search queries to analyze their relationship with target run-ups before official bid announcements. Thus, we specifically examine the varying impact of investor attention of different investor groups (institutional and individual investors) on target run-ups. The results let us conclude that target run-ups of smaller, unprofitable, and growth firms are significantly related with social media coverage on HotCopper. On the contrary, similar firms that lack media coverage do not experience a significant target run-up prior to a bid announcement. Target run-ups of larger capitalization stocks are, on the other hand, more sensitive to analyst recommendations. The results are consistent with the anecdotal evidence that smaller firms are usually less covered by analysts. Social media closes the information gap for small firms in this perspective. Google search inquiries for target firms are not found to be significantly related to target run-ups. The overall findings of the second empirical work support the market expectation hypothesis. In this regard, social media contributes to the increase of market efficiency and partially closes informational blind spots for smaller firms which might exist due to inefficient allocations of resources or costly information sourcing for smaller firms. The fifth chapter comprises the last empirical work of this dissertation and explores the relationship between media press sentiment and capital markets. We specifically examine the im-pact of aggregated news sentiment indices on the cross-section of returns in the asset pricing context. The literature around asset pricing especially focuses on the determination of risk premia that help to explain stock returns. A central question of our third empirical work is, therefore, whether stock returns are associated with their underlying risk or whether these returns are just a result of irrational market movements in the spirit of the behavioral finance theory. We calculate monthly aggregated news sentiment indices based on more than 120 million unique classified news articles from the Ravenpack News Analytics database between 2000 and 2017. Thus, we construct monthly zero-investment portfolios that go long on (sell) stocks which exhibit on average positive (negative) news sentiment in the previous month. The portfolio yields an annual return of 7.5% even if we control for widely-accepted risk fac-tors, such as market, size, momentum, liquidity, profitability, and investments. The results are mainly driven by positive news sentiment. Hence, we refer this premium to the “premium on optimism”. One possible explanation could be the persistent positive news coverage in the respective time period. The probability of the publication of good news is in particularly high-er if a firm experienced positive news in the prior months. The total results of our third empirical work support the view that news sentiment reflects a risk factor. The overall results of this dissertation have several implications for firms, investors, regulators and researchers in the field of behavioral finance. Firms must learn today to early anticipate crowd movements on (social) media and to deal with putatively fake news. The investor relations department of a firm must engage in this topic more sophistically content-wise and in the communicative interaction with its stakeholders. Selective communication strategies for specific firm events are required to early prevent a potentially negative public perception of the firm. Fake news and volatile markets are also gaining in importance for regulators. The identification of manipulative activities or the stabilization of financial markets in the presence of ambiguous information is of special interest for regulators. This task is even more relevant in the time of increased digitalization of media channels and the networks behind them. The more important is, hence, a better understanding of the stakeholders in financial markets and their actions for the functionality of efficient markets. Finally, the results of this dissertation create new connection points for future research. The asymmetric role of investor sentiment and its underlying mechanism are still controversial and elusive. Current studies especially fail to shed light on the long-term impact of investor sentiment on capital markets. This dissertation, hence, provides a substantiated baseline for future empirical work. Also, this work could not fully answer the question in which situation investors specifically use different media channels for information sourcing and dissemination. An intraday-based analysis on various media channels could provide new answers to this question. In summary, this dissertation shows that investor sentiment is an integral part of today’s financial markets and its important role cannot be anymore neglected by advocates of the traditional finance theory
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