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After he had only tightly lost the election in July 2006, Andrés Manuel López Obrador and his Coalición claimed fraud and asserted that unfair conditions during the campaign had diminished his chances to win the presidency. The paper investigates this latter allegation centering on a perceived campaign of hate, unequal access to campaign resources and malicious treatment by the mass media. It further analyzes the mass media’s performance during the conflictual post electoral period until the final decision of the Federal Electoral Tribunal on September 5th, 2006. While the media’s performance during the campaign tells us about their compliance with fair media coverage mechanisms that have been implemented by electoral reforms in the 1990s, the mass media is uncontained by such measures after the election. Thus, their mode of coverage of the postelectoral conflicts allows us to “test” the mass media’s transformation to a more unbiased, social responsible “fourth estate”. Finally the paper scrutinizes whether the claims of fraud and the protests by the leftist movement resulted in lower levels of institutional trust and democratic support. The analysis of the media performance is based on data provided by the Federal Electoral Institute (IFE). Its Media Monitor encompassed more than 150 TV stations, 240 radio stations and 200 press publications. However, there is no comparable data available for the postelectoral period. Interviews with Mexican media experts, which the author has conducted during the postelectoral period, serve as empirical basis for the second part. Data on the public opinions and attitudes of Mexican citizens are taken from the 2007 Latinobarometro, the 2006 Encuesta Nacional and several polls conducted by Grupo Reforma. The results do not support López Obradors notions. Even though a strong party bias is characteristic of the Mexican media system, all findings hint at a continuity of balanced campaign coverage and fair access to mass media publicity. Coverage during the postelectoral period was more polarized, yet both sides remained at least partially open for oppositional views. The claims of fraud, mass protest mobilization and anti-institutional discourse by Lopez Obrador’s leftist movement seem not to have caused significant loss in institutional trust, support of and satisfaction with democracy, even though these levels remain quite low.
Iconographic representations on ancient artifacts are described in many existing databases and literature as human readable text. We applied Natural Language Processing (NLP) approaches in order to extract the semantics out of these textual descriptions and in this way enable semantic searches over them. This allows more sophisticated requests compared to the common existing keyword searches. As we show in our experiments based on numismatic datasets, the approach is generic in the sense that once the system is trained on one dataset, it can be applied without any further manual work also to datasets that have similar content. Of course, additional adaptions would further improve the results. Since the approach requires manual work only during the training phase, it can easily be applied to huge datasets without manual work and therefore without major extra costs. In fact, in our experience bigger datasets generate even better results because there is more data for training. Since our approach is not bound to a certain domain and the numismatic datasets are just an example, it could serve as a blueprint for many other areas. It could also help to build bridges between disciplines since textual iconographic descriptions are to be found also for pottery, sculpture and elsewhere.