How to Do Things With Words

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

Social Network Analysis

We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument and a condition on the relation between the instrument and the participation status is sufficient for identification of a local average treatment effect for those who can be induced to change their participation status by changing the value of the instrument. Finally we derive the probability limit of the standard IV estimator under these conditions. It is seen to be a weighted average of local average treatment effects.

Evolution and Rationality Some Recent Game-Theoretic Results. Identification and Estimation of Local Average Treatment Effects

Twitter is a social media giant famous for the exchange of short, 140-character messages called "tweets". In the scientific community, the microblogging site is known for openness in sharing its data. It provides a glance into its millions of users and billions of tweets through a "Streaming API" which provides a sample of all tweets matching some parameters preset by the API user. The API service has been used by many researchers, companies, and governmental institutions that want to extract knowledge in accordance with a diverse array of questions pertaining to social media. The essential drawback of the Twitter API is the lack of documentation concerning what and how much data users get. This leads researchers to question whether the sampled data is a valid representation of the overall activity on Twitter. In this work we embark on answering this question by comparing data collected using Twitter's sampled API service with data collected using the full, albeit costly, Firehose stream that includes every single published tweet. We compare both datasets using common statistical metrics as well as metrics that allow us to compare topics, networks, and locations of tweets. The results of our work will help researchers and practitioners understand the implications of using the Streaming API.

Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose

On 3 November 1948, the day after Harry Truman won the United States presidential elections, the Chicago Tribune published one of the most famous erroneous headlines in newspaper history: “Dewey Defeats Truman” ( 1 , 2 ). The headline was informed by telephone surveys, which had inadvertently undersampled Truman supporters ( 1 ). Rather than permanently discrediting the practice of polling, this event led to the development of more sophisticated techniques and higher standards that produce the more accurate and statistically rigorous polls conducted today ( 3 ).

https://science.sciencemag.org/content/sci/346/6213/1063.full.pdf

Social media for large studies of behavior

/pdf/is-the-sample-good-enough-comparing-data-from-twitter-s-5ddwms6swi.pdf

Social media are, on the one hand, a highly beneficial environment for word-of-mouth (WOM) propagation of new ideas and products, and this has increasingly made them a focus of marketing communications. On the other hand, companies and their brands as well as politicians, governmental institutions, and celebrities have increasingly been facing the impact of negative online WOM and complaint behavior. In reaction to any questionable statement or activity, social media users can create huge waves of outrage within just a few hours. These so-called online firestorms pose new challenges for marketing communications. In this article, we group observations from recent online firestorms, identify related social and economic science theories, and derive generalized factors that form the basis for the proliferation of these dynamics. Furthermore, we discuss the consequences of online firestorms for marketing communications, and offer courses of action for marketers to navigate through crises of negative online WOM.

Understanding online firestorms: Negative word-of-mouth dynamics in social media networks

Geotagged tweets are an exciting and increasingly popular data source, but like all social media data, they potentially have biases in who are represented. Motivated by this, we investigate the question, 'are users of geotagged tweets randomly distributed over the US population'? We link approximately 144 million geotagged tweets within the US, representing 2.6m unique users, to high-resolution Census population data and carry out a statistical test by which we answer this question strongly in the negative. We utilize spatial models and integrate further Census data to investigate the factors associated with this nonrandom distribution. We find that, controlling for other factors, population has no effect on the number of geotag users, and instead it is predicted by a number of factors including higher median income, being in an urban area, being further east or on a coast, having more young people, and having high Asian, Black or Hispanic/Latino populations.

https://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/viewFile/10662/10551

Jürgen Pfeffer

Papers

Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose

Social media for large studies of behavior

Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose

Understanding online firestorms: Negative word-of-mouth dynamics in social media networks

Population Bias in Geotagged Tweets