TopExNet: Entity-Centric Network Topic Exploration in News Streams

The recent introduction of entity-centric implicit network representations of unstructured text offers novel ways for exploring entity relations in document collections and streams efficiently and interactively. Here, we present TopExNet as a tool for exploring entity-centric network topics in streams of news articles. The application is available as a web service at https://topexnet.ifi.uni-heidelberg.de/ .Comment: Published in Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, VIC, Australia, February 11-15, 201

Implicit Entity Networks: A Versatile Document Model

The time in which we live is often referred to as the Information Age. However, it can also aptly be characterized as an age of constant information overload. Nowhere is this more present than on the Web, which serves as an endless source of news articles, blog posts, and social media messages. Of course, this overload is even greater in professions that handle the creation or extraction of information and knowledge, such as journalists, lawyers, researchers, clerks, or medical professionals. The volume of available documents and the interconnectedness of their contents are both a blessing and a curse for the contemporary information consumer. On the one hand, they provide near limitless information, but on the other hand, their consumption and comprehension requires an amount of time that many of us cannot spare. As a result, automated extraction, aggregation, and summarization techniques have risen in popularity, even though they are a long way from being comprehensive. When we, as humans, are faced with an overload of information, we tend to look for patterns that bring order into the chaos. In news, we might identify familiar political figures or celebrities, whereas we might look for expressive symptoms in medicine, or precedential cases in law. In other words, we look for known entities as reference points, and then explore the content along the lines of their relations to others entities. Unfortunately, this approach is not reflected in current document models, which do not provide a similar focus on entities. As a direct result, the retrieval of entity-centric knowledge and relations from a flood of textual information becomes more difficult than it has to be, and the inclusion of external knowledge sources is impeded. In this thesis, we introduce implicit entity networks as a comprehensive document model that addresses this shortcoming and provides a holistic representation of document collections and document streams. Based on the premise of modelling the cooccurrence relations between terms and entities as first-class citizens, we investigate how the resulting network structure facilitates efficient and effective entity-centric search, and demonstrate the extraction of complex entity relations, as well as their summarization. We show that the implicit network model is fully compatible with dynamic streams of documents. Furthermore, we introduce document aggregation methods that are sensitive to the context of entity mentions, and can be used to distinguish between different entity relations. Beyond the relations of individual entities, we introduce network topics as a novel and scalable method for the extraction of topics from collections and streams of documents. Finally, we combine the insights gained from these applications in a versatile hypergraph document model that bridges the gap between unstructured text and structured knowledge sources

Who Is Listening? Spokesperson Effect on Communicating Social and Physical Distancing Measures During the COVID-19 Pandemic

Effective communication during a pandemic, such as the current COVID-19 crisis, can save lives. At the present time, social and physical distancing measures are the lead strategy in combating the spread of COVID-19. In this study, a survey was administered to 705 adults from Switzerland about their support and practice of social distancing measures to examine if their responses depended on (1) whether these measures were supported by a government official or an internationally recognized celebrity as a spokesperson, (2) whether this spokesperson was liked, and (3) the respondent's age. We also considered several attitudinal and demographic variables that may influence the degree to which people support and comply with social distancing measures. We found that the government official was more effective in eliciting responses supportive of social distancing, particularly as manifested in the stated current compliance with social distancing measures. The effect was substantially stronger among older respondents, although these respondents expressed a lower risk perception. Although there was a general trend for greater endorsement of the social distancing measures among participants who liked the spokesperson, this was non-significant. In addition, respondents' greater support and compliance was positively associated with (1) higher concern for the current situation, (2) higher concern for the well-being of others, and (3) greater belief that others were practicing social distancing, and negatively with (4) greater self-reported mobility. Current compliance correlated negatively with (5) household size. Since different parts of the population appear to have different perceptions of risk and crisis, our preliminary results suggest that different spokespersons may be needed for different segments of the population, and particularly for younger and older populations. The development of evidence-based knowledge is required to further identify who would be the most effective spokesperson, and in particular to groups with low risk perception and low compliance

The effect of spokesperson attribution on public health message sharing during the COVID-19 pandemic

It is urgent to understand how to effectively communicate public health messages during the COVID-19 pandemic. Previous work has focused on how to formulate messages in terms of style and content, rather than on who should send them. In particular, little is known about the impact of spokesperson selection on message propagation during times of crisis. We report on the effectiveness of different public figures at promoting social distancing among 12,194 respondents from six countries that were severely affected by the COVID-19 pandemic at the time of data collection. Across countries and demographic strata, immunology expert Dr. Anthony Fauci achieved the highest level of respondents’ willingness to reshare a call to social distancing, followed by a government spokesperson. Celebrity spokespersons were least effective. The likelihood of message resharing increased with age and when respondents expressed positive sentiments towards the spokesperson. These results contribute to the development of evidence-based knowledge regarding the effectiveness of prominent official and non-official public figures in communicating public health messaging in times of crisis. Our findings serve as a reminder that scientific experts and governments should not underestimate their power to inform and persuade in times of crisis and underscore the crucial importance of selecting the most effective messenger in propagating messages of lifesaving information during a pandemic

Quotatives Indicate Decline in Objectivity in U.S. Political News

According to journalistic standards, direct quotes should be attributed to sources with objective quotatives such as "said" and "told", as nonobjective quotatives, like "argued" and "insisted" would influence the readers' perception of the quote and the quoted person. In this paper, we analyze the adherence to this journalistic norm to study trends in objectivity in political news across U.S. outlets of different ideological leanings. We ask: 1) How has the usage of nonobjective quotatives evolved? and 2) How do news outlets use nonobjective quotatives when covering politicians of different parties? To answer these questions, we developed a dependency-parsing-based method to extract quotatives and applied it to Quotebank, a web-scale corpus of attributed quotes, obtaining nearly 7 million quotes, each enriched with the quoted speaker's political party and the ideological leaning of the outlet that published the quote. We find that while partisan outlets are the ones that most often use nonobjective quotatives, between 2013 and 2020, the outlets that increased their usage of nonobjective quotatives the most were "moderate" centrist news outlets (around 0.6 percentage points, or 20% in relative percentage over 7 years). Further, we find that outlets use nonobjective quotatives more often when quoting politicians of the opposing ideology (e.g., left-leaning outlets quoting Republicans), and that this "quotative bias" is rising at a swift pace, increasing up to 0.5 percentage points, or 25% in relative percentage, per year. These findings suggest an overall decline in journalistic objectivity in U.S. political news.Comment: Repo: https://github.com/epfl-dlab/quotative_bia